{"data":{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36424614", "kind": "science-record", "revision": 3, "content_hash": "sha256:59b2a914ddbf8e77c6e8a73333f91d10b98e4f192aa7ff8fa56f280c8913b20f", "created_at": "2026-11-01T19:00:53Z", "updated_at": "2026-11-01T22:08:09Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Establishment of an ovarian cancer omentum metastasis-related prognostic model by integrated analysis of scRNA-seq and bulk RNA-seq.", "doi": "10.1186/s13048-022-01059-0", "pmid": "36424614", "authors": ["Dongni Zhang", "Wenping Lu", "Shasha Cui", "Heting Mei", "Xiaoqing Wu", "Zhili Zhuo"], "journal": "Journal of ovarian research", "year": 2022, "abstract": "OBJECTIVE: Ovarian cancer has the highest mortality rate among gynecological malignant tumors, and it preferentially metastasizes to omental tissue, leading to intestinal obstruction and death. scRNA-seq is a powerful technique to reveal tumor heterogeneity. Analyzing omentum metastasis of ovarian cancer at the single-cell level may be more conducive to exploring and understanding omentum metastasis and prognosis of ovarian cancer at the cellular function and genetic levels.\nMETHODS: The omentum metastasis site scRNA-seq data of GSE147082 were acquired from the GEO (Gene Expression Omnibus) database, and single cells were clustered by the Seruat package and annotated by the SingleR package. Cell differentiation trajectories were reconstructed through the monocle package. The ovarian cancer microarray data of GSE132342 were downloaded from GEO and were clustered by using the ConsensusClusterPlus package into omentum metastasis-associated clusters according to the marker genes gained from single-cell differentiation trajectory analysis. The tumor microenvironment (TME) and immune infiltration differences between clusters were analyzed by the estimate and CIBERSORT packages. The expression matrix of genes used to cluster GSE132342 patients was extracted from bulk RNA-seq data of TCGA-OV (The Cancer Genome Atlas ovarian cancer), and least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression were performed to establish an omentum metastasis-associated gene (OMAG) signature. The signature was then tested by GSE132342 data. Finally, the clinicopathological characteristics of TCGA-OV were screened by univariate and multivariate Cox regression analysis to draw the nomogram.\nRESULTS: A total of 9885 cells from 6 patients were clustered into 18 cell clusters and annotated into 14 cell types. Reconstruction of differentiation trajectories divided the cells into 5 branches, and a total of 781 cell trajectory-related characteristic genes were obtained. A total of 3769 patients in GSE132342 were subtyped into 3 clusters by 74 cell trajectory-related characteristic genes. Kaplan-Meier (K-M) survival analysis showed that the prognosis of cluster 2 was the worst, P < 0.001. The TME analysis showed that the ESTIMATE score and stromal score in cluster 2 were significantly higher than those in the other two clusters, P < 0.001. The immune infiltration analysis showed differences in the fraction of 8 immune cells among the 3 clusters, P < 0.05. The expression data of 74 genes used for GEO clustering were extracted from 379 patients in TCGA-OV, and combined with survival information, 10 candidates for OMAGs were filtered by LASSO. By using multivariate Cox regression, the 6-OMAGs signature was established as RiskScore = 0.307*TIMP3 + 3.516*FBN1-0.109*IGKC + 0.209*RPL21 + 0.870*UCHL1 + 0.365*RARRES1. Taking TCGA-OV as the training set and GSE132342 as the test set, receiver operating characteristic (ROC) curves were drawn to verify the prognostic value of 6-OMAGs. Screened by univariate and multivariate Cox regression analysis, 3 (age, cancer status, primary therapy outcome) of 5 clinicopathological characteristics were used to construct the nomogram combined with risk score.\nCONCLUSION: We constructed an ovarian cancer prognostic model related to omentum metastasis composed of 6-OMAGs and 3 clinicopathological features and analyzed the potential mechanism of these 6-OMAGs in ovarian cancer omental metastasis.", "claims": [{"id": "claim-method", "statement": "The glmnet [27] (Version: 4.1-3) and survival (Version: 3.2–13) R packages were used to perform LASSO Cox regression analysis and 10-fold cross-validation to narrow the range of OMAG candidate genes. The OMAG candidates were then screened again by Cox multivariate regression analysis to determine the ultimate OMAG risk model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36424614", "locator": "sections[9] \"Construction of a nomogram model in accordance with the OMAG risk signature\" paragraph 2", "quote": "The glmnet [27] (Version: 4.1-3) and survival (Version: 3.2–13) R packages were used to perform LASSO Cox regression analysis and 10-fold cross-validation to narrow the range of OMAG candidate genes. The OMAG candidates were then screened again by Cox multivariate regression analysis to determine the ultimate OMAG risk model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Please contact the corresponding author Wenping Lu (lu_wenping@sina.com).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36424614", "locator": "sections[26] \"Availability of data and materials\" paragraph 2", "quote": "Please contact the corresponding author Wenping Lu (lu_wenping@sina.com)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "We constructed an ovarian cancer prognostic model related to omentum metastasis composed of 6-OMAGs and 3 clinicopathological features and analyzed the potential mechanism of these 6-OMAGs in ovarian cancer omental metastasis.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36424614", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "We constructed an ovarian cancer prognostic model related to omentum metastasis composed of 6-OMAGs and 3 clinicopathological features and analyzed the potential mechanism of these 6-OMAGs in ovarian cancer omental metastasis."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "We constructed an ovarian cancer prognostic model related to omentum metastasis composed of 6-OMAGs and 3 clinicopathological features and analyzed the potential mechanism of these 6-OMAGs in ovarian cancer omental metastasis.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:36424614"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE132342"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE132342"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36424614", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s13048-022-01059-0", "sha256": "sha256:eb1bb81570ca056c6d072c7cda5c46e2c29c6af6c12973f414e54769c465cf4c", "bytes": 57923, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s13048-022-01059-0", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9686070", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE132342", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE132342", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE132342", "version": null, "note": "Please contact the corresponding author Wenping Lu (lu_wenping@sina.com).", "structure": {"repository": "GEO", "access_route": 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"GSM3857372", "GSM3857373", "GSM3857374", "GSM3857375", "GSM3857376", "GSM3857377", "GSM3857378", "GSM3857379", "GSM3857380", "GSM3857381", "GSM3857382", "GSM3857383", "GSM3857384", "GSM3857385", "GSM3857386", "GSM3857387", "GSM3857388", "GSM3857389", "GSM3857390", "GSM3857391", "GSM3857392", "GSM3857393", "GSM3857394", "GSM3857395", "GSM3857396", "GSM3857397", "GSM3857398", "GSM3857399", "GSM3857400", "GSM3857401", "GSM3857402", "GSM3857403", "GSM3857404", "GSM3857405", "GSM3857406", "GSM3857407", "GSM3857408", "GSM3857409", "GSM3857410", "GSM3857411", "GSM3857412", "GSM3857413", "GSM3857414", "GSM3857415", "GSM3857416", "GSM3857417", "GSM3857418", "GSM3857419", "GSM3857420", "GSM3857421", "GSM3857422", "GSM3857423", "GSM3857424", "GSM3857425", "GSM3857426", "GSM3857427", "GSM3857428", "GSM3857429", "GSM3857430", "GSM3857431", "GSM3857432", "GSM3857433", "GSM3857434", "GSM3857435", "GSM3857436", "GSM3857437", "GSM3857438", "GSM3857439", "GSM3857440", "GSM3857441", "GSM3857442", "GSM3857443", "GSM3857444", "GSM3857445", "GSM3857446", "GSM3857447", "GSM3857448", "GSM3857449", "GSM3857450", "GSM3857451", "GSM3857452", "GSM3857453", "GSM3857454", "GSM3857455", "GSM3857456", "GSM3857457", "GSM3857458", "GSM3857459", "GSM3857460", "GSM3857461", "GSM3857462", "GSM3857463", "GSM3857464", "GSM3857465", "GSM3857466", "GSM3857467", "GSM3857468", "GSM3857469", "GSM3857470", "GSM3857471", "GSM3857472", "GSM3857473", "GSM3857474", "GSM3857475", "GSM3857476", "GSM3857477", "GSM3857478", "GSM3857479", "GSM3857480", "GSM3857481", "GSM3857482", "GSM3857483", "GSM3857484", "GSM3857485", "GSM3857486", "GSM3857487", "GSM3857488", "GSM3857489", "GSM3857490", "GSM3857491", "GSM3857492", "GSM3857493", "GSM3857494", "GSM3857495", "GSM3857496", "GSM3857497", "GSM3857498", "GSM3857499", "GSM3857500", "GSM3857501", "GSM3857502", "GSM3857503", "GSM3857504", "GSM3857505", "GSM3857506", "GSM3857507", "GSM3857508", "GSM3857509", "GSM3857510", "GSM3857511", "GSM3857512", "GSM3857513", "GSM3857514", "GSM3857515", "GSM3857516", "GSM3857517", "GSM3857518", "GSM3857519", "GSM3857520", "GSM3857521", "GSM3857522", "GSM3857523", "GSM3857524", "GSM3857525", "GSM3857526", "GSM3857527", "GSM3857528", "GSM3857529", "GSM3857530", "GSM3857531", "GSM3857532", "GSM3857533", "GSM3857534", "GSM3857535", "GSM3857536", "GSM3857537", "GSM3857538", "GSM3857539", "GSM3857540", "GSM3857541", "GSM3857542", "GSM3857543", "GSM3857544", "GSM3857545", "GSM3857546", "GSM3857547", "GSM3857548", "GSM3857549", "GSM3857550", "GSM3857551", "GSM3857552", "GSM3857553", "GSM3857554", "GSM3857555", "GSM3857556", "GSM3857557", "GSM3857558", "GSM3857559", "GSM3857560", "GSM3857561", "GSM3857562", "GSM3857563", "GSM3857564", "GSM3857565", "GSM3857566", "GSM3857567", "GSM3857568", "GSM3857569", "GSM3857570", "GSM3857571", "GSM3857572", "GSM3857573", "GSM3857574", "GSM3857575", "GSM3857576", "GSM3857577", "GSM3857578", "GSM3857579", "GSM3857580", "GSM3857581", "GSM3857582", "GSM3857583", "GSM3857584", "GSM3857585", "GSM3857586", "GSM3857587", "GSM3857588", "GSM3857589", "GSM3857590", "GSM3857591", "GSM3857592", "GSM3857593", "GSM3857594", "GSM3857595", "GSM3857596", "GSM3857597", "GSM3857598", "GSM3857599", "GSM3857600", "GSM3857601", "GSM3857602", "GSM3857603", "GSM3857604", "GSM3857605", "GSM3857606", "GSM3857607", "GSM3857608", "GSM3857609", "GSM3857610", "GSM3857611", "GSM3857612", "GSM3857613", "GSM3857614", "GSM3857615", "GSM3857616", "GSM3857617", "GSM3857618", "GSM3857619", "GSM3857620", "GSM3857621", "GSM3857622", "GSM3857623", "GSM3857624", "GSM3857625", "GSM3857626", "GSM3857627", "GSM3857628", "GSM3857629", "GSM3857630", "GSM3857631", "GSM3857632", "GSM3857633", "GSM3857634", "GSM3857635", "GSM3857636", "GSM3857637", "GSM3857638", "GSM3857639", "GSM3857640", "GSM3857641", "GSM3857642", "GSM3857643", "GSM3857644", "GSM3857645", "GSM3857646", "GSM3857647", "GSM3857648", "GSM3857649", "GSM3857650", "GSM3857651", "GSM3857652", "GSM3857653", "GSM3857654", "GSM3857655", "GSM3857656", "GSM3857657", "GSM3857658", "GSM3857659", "GSM3857660", "GSM3857661", "GSM3857662", "GSM3857663", "GSM3857664", "GSM3857665", "GSM3857666", "GSM3857667", "GSM3857668", "GSM3857669", "GSM3857670", "GSM3857671", "GSM3857672", "GSM3857673", "GSM3857674", "GSM3857675", "GSM3857676", "GSM3857677", "GSM3857678", "GSM3857679", "GSM3857680", "GSM3857681", "GSM3857682", "GSM3857683", "GSM3857684", "GSM3857685", "GSM3857686", "GSM3857687", "GSM3857688", "GSM3857689", "GSM3857690", "GSM3857691", "GSM3857692", "GSM3857693", "GSM3857694", "GSM3857695", "GSM3857696", "GSM3857697", "GSM3857698", "GSM3857699", "GSM3857700", "GSM3857701", "GSM3857702", "GSM3857703", "GSM3857704", "GSM3857705", "GSM3857706", "GSM3857707", "GSM3857708", "GSM3857709", "GSM3857710", "GSM3857711", "GSM3857712", "GSM3857713", "GSM3857714", "GSM3857715", "GSM3857716", "GSM3857717", "GSM3857718", "GSM3857719", "GSM3857720", "GSM3857721", "GSM3857722", "GSM3857723", "GSM3857724", "GSM3857725", "GSM3857726", "GSM3857727", "GSM3857728", "GSM3857729", "GSM3857730", "GSM3857731", "GSM3857732", "GSM3857733", "GSM3857734", "GSM3857735", "GSM3857736", "GSM3857737", "GSM3857738", "GSM3857739", "GSM3857740", "GSM3857741", "GSM3857742", "GSM3857743", "GSM3857744", "GSM3857745", "GSM3857746", "GSM3857747", "GSM3857748", "GSM3857749", "GSM3857750", "GSM3857751", "GSM3857752", "GSM3857753", "GSM3857754", "GSM3857755", "GSM3857756", "GSM3857757", "GSM3857758", "GSM3857759", "GSM3857760", "GSM3857761", "GSM3857762", "GSM3857763", "GSM3857764", "GSM3857765", "GSM3857766", "GSM3857767", "GSM3857768", "GSM3857769", "GSM3857770", "GSM3857771", "GSM3857772", "GSM3857773", "GSM3857774", "GSM3857775", "GSM3857776", "GSM3857777", "GSM3857778", "GSM3857779", "GSM3857780", "GSM3857781", "GSM3857782", "GSM3857783", "GSM3857784", "GSM3857785", "GSM3857786", "GSM3857787", "GSM3857788", "GSM3857789", "GSM3857790", "GSM3857791", "GSM3857792", "GSM3857793", "GSM3857794", "GSM3857795", "GSM3857796", "GSM3857797", "GSM3857798", "GSM3857799", "GSM3857800", "GSM3857801", "GSM3857802", "GSM3857803", "GSM3857804", "GSM3857805", "GSM3857806", "GSM3857807", "GSM3857808", "GSM3857809", "GSM3857810", "GSM3857811", "GSM3857812", "GSM3857813", "GSM3857814", "GSM3857815", "GSM3857816", "GSM3857817", "GSM3857818", "GSM3857819", "GSM3857820", "GSM3857821", "GSM3857822", "GSM3857823", "GSM3857824", "GSM3857825", "GSM3857826", "GSM3857827", "GSM3857828", "GSM3857829", "GSM3857830", "GSM3857831", "GSM3857832", "GSM3857833", "GSM3857834", "GSM3857835", "GSM3857836", "GSM3857837", "GSM3857838", "GSM3857839", "GSM3857840", "GSM3857841", "GSM3857842", "GSM3857843", "GSM3857844", "GSM3857845", "GSM3857846", "GSM3857847", "GSM3857848", "GSM3857849", "GSM3857850", "GSM3857851", "GSM3857852", "GSM3857853", "GSM3857854", "GSM3857855", "GSM3857856", "GSM3857857", "GSM3857858", "GSM3857859", "GSM3857860", "GSM3857861", "GSM3857862", "GSM3857863", "GSM3857864", "GSM3857865", "GSM3857866", "GSM3857867", "GSM3857868", "GSM3857869", "GSM3857870", "GSM3857871", "GSM3857872", "GSM3857873", "GSM3857874", "GSM3857875", "GSM3857876", "GSM3857877", "GSM3857878", "GSM3857879", "GSM3857880", "GSM3857881", "GSM3857882", "GSM3857883", "GSM3857884", "GSM3857885", "GSM3857886", "GSM3857887", "GSM3857888", "GSM3857889", "GSM3857890", "GSM3857891", "GSM3857892", "GSM3857893", "GSM3857894", "GSM3857895", "GSM3857896", "GSM3857897", "GSM3857898", "GSM3857899", "GSM3857900", "GSM3857901", "GSM3857902", "GSM3857903", "GSM3857904", "GSM3857905", "GSM3857906", "GSM3857907", "GSM3857908", "GSM3857909", "GSM3857910", "GSM3857911", "GSM3857912", "GSM3857913", "GSM3857914", "GSM3857915", "GSM3857916", "GSM3857917", "GSM3857918", "GSM3857919", "GSM3857920", "GSM3857921", "GSM3857922", "GSM3857923", "GSM3857924", "GSM3857925", "GSM3857926", "GSM3857927", "GSM3857928", "GSM3857929", "GSM3857930", "GSM3857931", "GSM3857932", "GSM3857933", "GSM3857934", "GSM3857935", "GSM3857936", "GSM3857937", "GSM3857938", "GSM3857939", "GSM3857940", "GSM3857941", "GSM3857942", "GSM3857943", "GSM3857944", "GSM3857945", "GSM3857946", "GSM3857947", "GSM3857948", "GSM3857949", "GSM3857950", "GSM3857951", "GSM3857952", "GSM3857953", "GSM3857954", "GSM3857955", "GSM3857956", "GSM3857957", "GSM3857958", "GSM3857959", "GSM3857960", "GSM3857961", "GSM3857962", "GSM3857963", "GSM3857964", "GSM3857965", "GSM3857966", "GSM3857967", "GSM3857968", "GSM3857969", "GSM3857970", "GSM3857971", "GSM3857972", "GSM3857973", "GSM3857974", "GSM3857975", "GSM3857976", "GSM3857977", "GSM3857978", "GSM3857979", "GSM3857980", "GSM3857981", "GSM3857982", "GSM3857983", "GSM3857984", "GSM3857985", "GSM3857986", "GSM3857987", "GSM3857988", "GSM3857989", "GSM3857990", "GSM3857991", "GSM3857992", "GSM3857993", "GSM3857994", "GSM3857995", "GSM3857996", "GSM3857997", "GSM3857998", "GSM3857999", "GSM3858000", "GSM3858001", "GSM3858002", "GSM3858003", "GSM3858004", "GSM3858005", "GSM3858006", "GSM3858007", "GSM3858008", "GSM3858009", "GSM3858010", "GSM3858011", "GSM3858012", "GSM3858013", "GSM3858014", "GSM3858015", "GSM3858016", "GSM3858017", "GSM3858018", "GSM3858019", "GSM3858020", "GSM3858021", "GSM3858022", "GSM3858023", "GSM3858024", "GSM3858025", "GSM3858026", "GSM3858027", "GSM3858028", "GSM3858029", "GSM3858030", "GSM3858031", "GSM3858032", "GSM3858033", "GSM3858034", "GSM3858035", "GSM3858036", "GSM3858037", "GSM3858038", "GSM3858039", "GSM3858040", "GSM3858041", "GSM3858042", "GSM3858043", "GSM3858044", "GSM3858045", "GSM3858046", "GSM3858047", "GSM3858048", "GSM3858049", "GSM3858050", "GSM3858051", "GSM3858052", "GSM3858053", "GSM3858054", "GSM3858055", "GSM3858056", "GSM3858057", "GSM3858058", "GSM3858059", "GSM3858060", "GSM3858061", "GSM3858062", "GSM3858063", "GSM3858064", "GSM3858065", "GSM3858066", "GSM3858067", "GSM3858068", "GSM3858069", "GSM3858070", "GSM3858071", "GSM3858072", "GSM3858073", "GSM3858074", "GSM3858075", "GSM3858076", "GSM3858077", "GSM3858078", "GSM3858079", "GSM3858080", "GSM3858081", "GSM3858082", "GSM3858083", "GSM3858084", "GSM3858085", "GSM3858086", "GSM3858087", "GSM3858088", "GSM3858089", "GSM3858090", "GSM3858091", "GSM3858092", "GSM3858093", "GSM3858094", "GSM3858095", "GSM3858096", "GSM3858097", "GSM3858098", "GSM3858099", "GSM3858100", "GSM3858101", "GSM3858102", "GSM3858103", "GSM3858104", "GSM3858105", "GSM3858106", "GSM3858107", "GSM3858108", "GSM3858109", "GSM3858110", "GSM3858111", "GSM3858112", "GSM3858113", "GSM3858114", "GSM3858115", "GSM3858116", "GSM3858117", "GSM3858118", "GSM3858119", "GSM3858120", "GSM3858121", "GSM3858122", "GSM3858123", "GSM3858124", "GSM3858125", "GSM3858126", "GSM3858127", "GSM3858128", "GSM3858129", "GSM3858130", "GSM3858131", "GSM3858132", "GSM3858133", "GSM3858134", "GSM3858135", "GSM3858136", "GSM3858137", "GSM3858138", "GSM3858139", "GSM3858140", "GSM3858141", "GSM3858142", "GSM3858143", "GSM3858144", "GSM3858145", "GSM3858146", "GSM3858147", "GSM3858148", "GSM3858149", "GSM3858150", "GSM3858151", "GSM3858152", "GSM3858153", "GSM3858154", "GSM3858155", "GSM3858156", "GSM3858157", "GSM3858158", "GSM3858159", "GSM3858160", "GSM3858161", "GSM3858162", "GSM3858163", "GSM3858164", "GSM3858165", "GSM3858166", "GSM3858167", "GSM3858168", "GSM3858169", "GSM3858170", "GSM3858171", "GSM3858172", "GSM3858173", "GSM3858174", "GSM3858175", "GSM3858176", "GSM3858177", "GSM3858178", "GSM3858179", "GSM3858180", "GSM3858181", "GSM3858182", "GSM3858183", "GSM3858184", "GSM3858185", "GSM3858186", "GSM3858187", "GSM3858188", "GSM3858189", "GSM3858190", "GSM3858191", "GSM3858192", "GSM3858193", "GSM3858194", "GSM3858195", "GSM3858196", "GSM3858197", "GSM3858198", "GSM3858199", "GSM3858200", "GSM3858201", "GSM3858202", "GSM3858203", "GSM3858204", "GSM3858205", "GSM3858206", "GSM3858207", "GSM3858208", "GSM3858209", "GSM3858210", "GSM3858211", "GSM3858212", "GSM3858213", "GSM3858214", "GSM3858215", "GSM3858216", "GSM3858217", "GSM3858218", "GSM3858219", "GSM3858220", "GSM3858221", "GSM3858222", "GSM3858223", "GSM3858224", "GSM3858225", "GSM3858226", "GSM3858227", "GSM3858228", "GSM3858229", "GSM3858230", "GSM3858231", "GSM3858232", "GSM3858233", "GSM3858234", "GSM3858235", "GSM3858236", "GSM3858237", "GSM3858238", "GSM3858239", "GSM3858240", "GSM3858241", "GSM3858242", "GSM3858243", "GSM3858244", "GSM3858245", "GSM3858246", "GSM3858247", "GSM3858248", "GSM3858249", "GSM3858250", "GSM3858251", "GSM3858252", "GSM3858253", "GSM3858254", "GSM3858255", "GSM3858256", "GSM3858257", "GSM3858258", "GSM3858259", "GSM3858260", "GSM3858261", "GSM3858262", "GSM3858263", "GSM3858264", "GSM3858265", "GSM3858266", "GSM3858267", "GSM3858268", "GSM3858269", "GSM3858270", "GSM3858271", "GSM3858272", "GSM3858273", "GSM3858274", "GSM3858275", "GSM3858276", "GSM3858277", "GSM3858278", "GSM3858279", "GSM3858280", "GSM3858281", "GSM3858282", "GSM3858283", "GSM3858284", "GSM3858285", "GSM3858286", "GSM3858287", "GSM3858288", "GSM3858289", "GSM3858290", "GSM3858291", "GSM3858292", "GSM3858293", "GSM3858294", "GSM3858295", "GSM3858296", "GSM3858297", "GSM3858298", "GSM3858299", "GSM3858300", "GSM3858301", "GSM3858302", "GSM3858303", "GSM3858304", "GSM3858305", "GSM3858306", "GSM3858307", "GSM3858308", "GSM3858309", "GSM3858310", "GSM3858311", "GSM3858312", "GSM3858313", "GSM3858314", "GSM3858315", "GSM3858316", "GSM3858317", "GSM3858318", "GSM3858319", "GSM3858320", "GSM3858321", "GSM3858322", "GSM3858323", "GSM3858324", "GSM3858325", "GSM3858326", "GSM3858327", "GSM3858328", "GSM3858329", "GSM3858330", "GSM3858331", "GSM3858332", "GSM3858333", "GSM3858334", "GSM3858335", "GSM3858336", "GSM3858337", "GSM3858338", "GSM3858339", "GSM3858340", "GSM3858341", "GSM3858342", "GSM3858343", "GSM3858344", "GSM3858345", "GSM3858346", "GSM3858347", "GSM3858348", "GSM3858349", "GSM3858350", "GSM3858351", "GSM3858352", "GSM3858353", "GSM3858354", "GSM3858355", "GSM3858356", "GSM3858357", "GSM3858358", "GSM3858359", "GSM3858360", "GSM3858361", "GSM3858362", "GSM3858363", "GSM3858364", "GSM3858365", 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"GSM3860070", "GSM3860071", "GSM3860072", "GSM3860073", "GSM3860074", "GSM3860075", "GSM3860076", "GSM3860077", "GSM3860078", "GSM3860079", "GSM3860080", "GSM3860081", "GSM3860082", "GSM3860083", "GSM3860084", "GSM3860085", "GSM3860086", "GSM3860087", "GSM3860088", "GSM3860089", "GSM3860090", "GSM3860091", "GSM3860092", "GSM3860093", "GSM3860094", "GSM3860095", "GSM3860096", "GSM3860097", "GSM3860098", "GSM3860099", "GSM3860100", "GSM3860101", "GSM3860102", "GSM3860103", "GSM3860104", "GSM3860105", "GSM3860106", "GSM3860107", "GSM3860108", "GSM3860109", "GSM3860110", "GSM3860111", "GSM3860112", "GSM3860113", "GSM3860114", "GSM3860115", "GSM3860116", "GSM3860117", "GSM3860118", "GSM3860119", "GSM3860120", "GSM3860121", "GSM3860122", "GSM3860123", "GSM3860124", "GSM3860125", "GSM3860126", "GSM3860127", "GSM3860128", "GSM3860129", "GSM3860130", "GSM3860131", "GSM3860132", "GSM3860133", "GSM3860134", "GSM3860135", "GSM3860136", "GSM3860137", "GSM3860138", "GSM3860139", "GSM3860140", 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"GSM3860212", "GSM3860213", "GSM3860214", "GSM3860215", "GSM3860216", "GSM3860217", "GSM3860218", "GSM3860219", "GSM3860220", "GSM3860221", "GSM3860222", "GSM3860223", "GSM3860224", "GSM3860225", "GSM3860226", "GSM3860227", "GSM3860228", "GSM3860229", "GSM3860230", "GSM3860231", "GSM3860232", "GSM3860233", "GSM3860234", "GSM3860235", "GSM3860236", "GSM3860237", "GSM3860238", "GSM3860239", "GSM3860240", "GSM3860241", "GSM3860242", "GSM3860243", "GSM3860244", "GSM3860245", "GSM3860246", "GSM3860247", "GSM3860248", "GSM3860249", "GSM3860250", "GSM3860251", "GSM3860252", "GSM3860253", "GSM3860254", "GSM3860255", "GSM3860256", "GSM3860257", "GSM3860258", "GSM3860259", "GSM3860260", "GSM3860261", "GSM3860262", "GSM3860263", "GSM3860264", "GSM3860265", "GSM3860266", "GSM3860267", "GSM3860268", "GSM3860269", "GSM3860270", "GSM3860271", "GSM3860272", "GSM3860273", "GSM3860274", "GSM3860275", "GSM3860276", "GSM3860277", "GSM3860278", "GSM3860279", "GSM3860280", "GSM3860281", "GSM3860282", "GSM3860283", "GSM3860284", "GSM3860285", "GSM3860286", "GSM3860287", "GSM3860288", "GSM3860289", "GSM3860290", "GSM3860291", "GSM3860292", "GSM3860293", "GSM3860294", "GSM3860295", "GSM3860296", "GSM3860297", "GSM3860298", "GSM3860299", "GSM3860300", "GSM3860301", "GSM3860302", "GSM3860303", "GSM3860304", "GSM3860305", "GSM3860306", "GSM3860307", "GSM3860308", "GSM3860309", "GSM3860310", "GSM3860311", "GSM3860312", "GSM3860313", "GSM3860314", "GSM3860315", "GSM3860316", "GSM3860317", "GSM3860318", "GSM3860319", "GSM3860320", "GSM3860321", "GSM3860322", "GSM3860323", "GSM3860324", "GSM3860325", "GSM3860326", "GSM3860327", "GSM3860328", "GSM3860329", "GSM3860330", "GSM3860331", "GSM3860332", "GSM3860333", "GSM3860334", "GSM3860335", "GSM3860336", "GSM3860337", "GSM3860338", "GSM3860339", "GSM3860340", "GSM3860341", "GSM3860342", "GSM3860343", "GSM3860344", "GSM3860345", "GSM3860346", "GSM3860347", "GSM3860348", "GSM3860349", "GSM3860350", "GSM3860351", "GSM3860352", "GSM3860353", "GSM3860354", "GSM3860355", "GSM3860356", "GSM3860357", "GSM3860358", "GSM3860359", "GSM3860360", "GSM3860361", "GSM3860362", "GSM3860363", "GSM3860364", "GSM3860365", "GSM3860366", "GSM3860367", "GSM3860368", "GSM3860369", "GSM3860370", "GSM3860371", "GSM3860372", "GSM3860373", "GSM3860374"], "sample_count": 3769, "group_sizes": null, "group_sizes_basis": null, "platform": "26748", "taxon": "Homo sapiens", "title": "A gene expression prognostic signature for overall survival in patients with high-grade serous ovarian cancer", "resolution_checked_at": "2026-11-01T18:46:34Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE147082", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE147082", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE147082", "version": null, "note": "Please contact the corresponding author Wenping Lu (lu_wenping@sina.com).", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Please contact the corresponding author Wenping Lu (lu_wenping@sina.com).", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM4416534", "GSM4416535", "GSM4416536", "GSM4416537", "GSM4416538", "GSM4416539"], "sample_count": 6, "group_sizes": null, "group_sizes_basis": null, "platform": "18573", "taxon": "Homo sapiens", "title": "Characterizing the tumor microenvironment of metastatic ovarian cancer by single cell transcriptomics", "resolution_checked_at": "2026-11-01T18:46:34Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE132342 not downloaded", "asset:data:geo:GSE132342 licence not read", "asset:data:geo:GSE147082 not downloaded", "asset:data:geo:GSE147082 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "survival_analysis", "specific": "cox_proportional_hazards", "label": "Survival analysis", "dsl_operations": ["cox_ph"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "The glmnet [27] (Version: 4.1-3) and survival (Version: 3.2–13) R packages were used to perform LASSO Cox regression analysis and 10-fold cross-validation to narrow the range of OMAG candidate genes. The OMAG candidates were then screened again by Cox multivariate regression analysis to determine the ultimate OMAG risk model."}}, {"id": "method-2", "family": "group_comparison", "specific": "wilcoxon", "label": "Group comparison", "dsl_operations": [], "missing_operations": ["group_comparison_wilcoxon"], "expressible": false, "parameters": {}, "data_shapes": ["two_or_more_groups_continuous"], "evidence": {"score": 0.8, "band": "high", "quote": "Wilcoxon"}}, {"id": "method-3", "family": "survival_analysis", "specific": "kaplan_meier", "label": "Survival analysis", "dsl_operations": ["survival_km"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.7, "band": "medium", "quote": "Kaplan-Meier"}}, {"id": "method-4", "family": "principal_component_analysis", "specific": "pca", "label": "Principal component analysis", "dsl_operations": ["pca_fit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["wide_numeric_matrix"], "evidence": {"score": 0.8, "band": "high", "quote": "PCA"}}, {"id": "method-5", "family": "differential_expression", "specific": "limma_voom", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["count_matrix_with_condition"], "evidence": {"score": 0.8, "band": "high", "quote": "limma"}}, {"id": "method-6", "family": "correlation_analysis", "specific": "pearson", "label": "Correlation analysis", "dsl_operations": ["pearson_r"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["paired_numeric_columns"], "evidence": {"score": 0.8, "band": "high", "quote": "Pearson correlation"}}, {"id": "method-7", "family": "correlation_analysis", "specific": "spearman", "label": "Correlation analysis", "dsl_operations": ["spearman_rho"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["paired_numeric_columns"], "evidence": {"score": 0.8, "band": "high", "quote": "Spearman correlation"}}, {"id": "method-8", "family": "contingency_analysis", "specific": "chi_square", "label": "Categorical (contingency table) analysis", "dsl_operations": ["chi_square_test"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["categorical_cross_tabulation"], "evidence": {"score": 0.8, "band": "high", "quote": "chi-square"}}], "targets": [], "gaps": ["method.reader_differs_from_extractor"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["cox_ph"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: cox_proportional_hazards."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE147082", "GSE132342"], "resolved_accessions": ["GSE147082", "GSE132342"], "statements": ["Please contact the corresponding author Wenping Lu (lu_wenping@sina.com)."], "basis": "The paper names GSE147082, GSE132342 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["cox_ph"], "assets": ["source:pmid:36424614", "data:geo:GSE132342"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE132342"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T18:16:40Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 16477, "completion_tokens": 2006, "cached_tokens": 128}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:36424614", "type": "paper", "label": "pmid:36424614"}, {"id": "source:pmid:36424614", "type": "manuscript", "label": "PMID 36424614 full text"}, {"id": "data:geo:GSE132342", "type": "dataset", "label": "GEO GSE132342"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:36424614", "type": "paper", "label": "pmid:36424614"}, {"id": "source:pmid:36424614", "type": "manuscript", "label": "PMID 36424614 full text"}, {"id": "data:geo:GSE132342", "type": "dataset", "label": "GEO GSE132342"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-36424614", "type": "extraction", "used": ["source:pmid:36424614"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE132342", "type": "accession_resolution", "used": ["data:geo:GSE132342"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:36424614", "parent_hash": "sha256:53eeb92a2a1d508bda8825bd18e1b3123bea84163c5a5766c634b1997795f2a8", "fork_of": null, "generation": 2}, "gaps": ["asset:data:geo:GSE132342 licence not read", "asset:data:geo:GSE132342 not downloaded", "asset:data:geo:GSE147082 licence not read", "asset:data:geo:GSE147082 not downloaded", "execution.not_run", "method.reader_differs_from_extractor", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "data_generation", "definition_id": "scrna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "unsupported", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["d prognostic model by integrated analysis of scRNA-seq and bulk RNA-seq.", "leading to intestinal obstruction and death. scRNA-seq is a powerful technique to reveal tumor hete", "levels. METHODS: The omentum metastasis site scRNA-seq data of GSE147082 were acquired from the GEO", "Materials and methods scRNA-seq data processing and analysis By using the R"], "order_basis": "paper_text_order:60", "id": "assay:1"}, {"role": "data_generation", "definition_id": "microarray", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["ough the monocle package. The ovarian cancer microarray data of GSE132342 were downloaded from GEO a", "log2FC| > 1 and adjusted P-value < 0.05. GEO microarray data processing and analysis The microarray", "microarray data processing and analysis The microarray data downloaded from GSE132342 were already"], "order_basis": "paper_text_order:889", "id": "assay:2"}, {"role": "data_generation", "definition_id": "apoptosis_assay", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.69, "evidence": ["MT markers, ECM-related genes, angiogenesis, apoptosis, DNA repair, G2/M checkpoint, inflammatory r"], "order_basis": "paper_text_order:12853", "id": "assay:3"}, {"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["cox_ph"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["The glmnet [27] (Version: 4.1-3) and survival (Version: 3.2–13) R packages were used to perform LASSO Cox regression analysis and 10-fold cross-validation to narrow the range of OMAG candidate genes. The OMAG candidates were then screened again by Cox multivariate regression analysis to determine the ultimate OMAG risk model."], "order_basis": "consumes_earlier_output", "id": "assay:4"}]},"meta":{"api_version":"v1","automation_ladder_snapshot":"ba34b49490d9bfaf","automation_ladder_caveats":["Method extraction is rule-based and has no hand-judged accuracy sample. Evidence bands order textual support; method agreement intervals are null (n=0), never probabilities. Work-type validation does not validate methods.","Operation coverage describes only explicitly named methods. It does not prove a complete protocol, compatible parameters, successful compilation or re-execution. Unrecognised or ambiguous methods require review.","Data reachability is a textual accession or public-data statement. No dataset was downloaded or checked for access, completeness or licence.","Capabilities are planning states seeded from the dated equipment register. No bench ownership, completed quote or partner booking is inferred. Procurement counts are dependencies, not guaranteed replications or financial returns.","Tier and blocker counts are deterministic assessments of this corpus, not measured agreement. R3 is reserved for procurement; unresolved methods and data require human work (R4). The most restrictive axis wins, with capability, method, data as tie-breaks. Resolving one blocker can leave another at the same tier."],"engine":"corpus_engine","engine_provider":"rule_based_v1+lex99e1195c7d6f","run_id":"run-20261023T122449232913","source":"esearch","source_detail":"PubMed mirror biomed_pubmed (read-only)","run_status":"complete","imported_at":"2026-11-03T20:16:52+00:00","generated_at":"2026-11-04T02:46:52+00:00","total_papers":5529,"total_assays_tracked":29,"automatable_assay_families":6,"validated":true,"caveat_count":9,"blocking_caveats":5,"caveats":[{"code":"low_evidence_dominates","basis":["4,152 of 5,529 classifications (75.1%) matched no cue evidence and sit at the 0.05 floor.","34 of 230 data_only calls (14.8%) are in that group."],"claim":"Work type and confidence bands","gloss":"Most records were classified from their title and framing alone, with assay-cue coverage at zero.","detail":"Most classifications in this run rest on no matched evidence at all. At the evidence floor the work type comes from title framing and a fixed prior, not from anything in the paper, so the published work-type split should be read as a rough triage signal rather than a measured distribution. The impact is bounded but not removed: since 2026-10-23 the engine excludes floor-confidence records from the shortlist entirely and publishes them separately as held out, so the recommendation is unaffected even though the census is.","status":"unverified","severity":"high"},{"code":"runtime_is_modelled","basis":["Runtime is summed from published per-assay hour figures. Timing awaits the first executed replication.","4,407 of 5,529 records (79.7%) model to 0.00 hours because no assay was matched, which the ranking then reads as \"cheapest\".","0 of 10 shortlisted records carry a 0.00-hour estimate."],"claim":"Estimated replication runtime","gloss":"The hour figures are estimates built from assay mentions; timing awaits the first executed replication.","detail":"The hour figures are a cost model derived from the bench steps implied by each matched assay family. The first executed replication will supply measured timing. Records without an assay match currently receive a zero-hour model value.","status":"unverified","severity":"high"},{"code":"signal_score_without_citations","basis":["5,529 of 5,529 records (100.0%) have no citation count from the source metadata."],"claim":"Signal score","gloss":"The signal score uses publication-type weight because the mirror supplied zero citation counts.","detail":"The source supplied no citation counts for any record in this run, so the signal score is publication-type weight only. The citation term of the ranking is inert here, and the shortlist is ordered by publication type alone.","status":"unverified","severity":"high"},{"code":"abstract_absent","basis":["1,667 of 5,529 records (30.2%) carry no abstract at all."],"claim":"Evidence base for classification","gloss":"Some records contain a title and publication type only, defining the available classification evidence.","detail":"Where the mirror contains a title and publication types only, those fields define the classifier evidence. A methods section is required for fuller automated and human review.","status":"unverified","severity":"medium"},{"code":"classifier_agreement_measured","basis":["Published two-way decision agreement 77.7% (310 of 399 decidable records) from a stratified hand-check of 438 real records.","Three-way diagnostic agreement 73.9% (288 of 390 decidable records).","data_only agreement 75.2% (100 of 133 decidable).","48 record(s) could not be decided from title and abstract and were excluded from the denominator.","56 analysis-only records were published under another work type, so the data_only share is not an upper bound."],"claim":"Published automatable decision","gloss":"This figure has been checked against a human reading the same papers, so we know how often it is right.","detail":"The published automatable / not-automatable distribution comes from a rule-based classifier that was hand-checked against a fixed ground-truth definition. The agreement rates below are measured, not estimated. The three-way work type remains a diagnostic and is reported separately.","status":"measured","severity":"high"},{"code":"llm_comparison_not_measured","basis":["rule_based_v1+lex99e1195c7d6f produced 5,529 of 5,529 classifications.","Measured on 438 labelled records; these are RULE-BASED figures, not model results."],"claim":"Rule-based versus LLM agreement","gloss":"Every number here is the rule engine’s own. Model comparison begins when a working API credential is connected and measured.","detail":"The published two-way decision agreed with a human reading on 310 of 399 decidable records (77.7%). The diagnostic three-way label agreed on 288 of 390 decidable records (73.9%). Every percentage in this run belongs to the rule engine. Model comparison begins when a working API credential is connected and measured.","status":"not_measured","severity":"high"},{"code":"confidence_is_a_band","basis":["low band: 135 of 210 hand-judged records were correct, 64.3% [57.6, 70.5] (95% CI).","medium band: 80 of 104 hand-judged records were correct, 76.9% [68.0, 84.0] (95% CI).","high band: 73 of 76 hand-judged records were correct, 96.0% [89.0, 98.7] (95% CI).","A fitted calibrated probability was built, measured and withdrawn: it overstated held-out accuracy and reorders nothing."],"claim":"Confidence band and evidence score","gloss":"The confidence band carries a measured accuracy interval; 0.85 represents relative rank strength.","detail":"Confidence on this run is a BAND plus its measured accuracy interval. The number stored alongside each record is a monotone evidence score that orders records; 0.85 represents relative rank strength. A fitted probability was withdrawn after it overstated held-out accuracy. Threshold on the band.","status":"measured","severity":"medium"},{"code":"three_way_split_is_diagnostic","basis":["The corpus is published at the two-way decision: automatable or not-automatable. The three-way split (data_only / physical / mixed) is published as a diagnostic with its measured reliability attached, not as a claim, because the rule engine's `mixed` call cannot be defended.","The engine called 22 record(s) mixed; 10 matched the human judgement (45.5%, 95% CI 26.9-65.3).","The engine's `mixed` call is right on 10/22 hand-judged records (45.5%, 95% CI 26.9%-65.3%); a constant `physical` guess scores 36.4%.","The engine's `mixed` call does not reliably beat a constant `physical` guess on the hand-judged records it labels mixed.","The sign of that difference flips between two independent samples, so the three-way refinement is not demonstrably informative.","The mixed stratum contributes a disproportionate share of errors relative to its size.","At title-only depth the physical/mixed split collapses: a title says that work was done, not whether it was also analysed."],"claim":"The published decision is two-way","gloss":"The three-way split is shown for diagnosis only. The published split is two ways.","detail":"Count and filter the corpus on automatable versus not-automatable. The data_only / physical / mixed split remains an internal engine diagnostic because the mixed call has lower measured reliability.","status":"measured","severity":"medium"},{"code":"automatable_flag_unproven","basis":["6 of 29 assay families are marked automatable now: meta_analysis, machine_learning, survival_analysis, pca, molecular_docking, differential_expression.","The flag is a rule over assay family and work type; validation begins with the first executed replication.","It inherits the 75.2% data_only agreement rate, because the flag requires the record to be analysis-only."],"claim":"Automatable now","gloss":"This flag marks assay families the rules classify as data-only. The first executed replication will validate it.","detail":"This flag marks families the rules consider replicable from data alone. Its validity depends on the analysis-only classification and will be measured through an executed replication.","status":"unverified","severity":"medium"}],"confidence_policy":"band_with_measured_interval","confidence_contract":{"policy":"band_with_measured_interval","summary":"Confidence is reported as a BAND with a measured accuracy interval. The raw evidence score supplies monotone ordering. A fitted point estimate was withdrawn after it overstated held-out accuracy.","evidence_score":"A monotone evidence score in [0,1] built from cue mass, axis separation and text coverage. Use it to order records. Interpret 0.85 as relative rank strength.","confidence_band":"low | medium | high. Consumers threshold on this value.","band_accuracy":"Wilson 95% interval on the measured agreement of the hand-judged records in this band.","calibrated_confidence":"WITHDRAWN. Reintroduction requires held-out evidence demonstrating reliable estimates across every bin."},"band_accuracy":{"low":{"n":210,"low":0.576,"band":"low","high":0.7046,"agree":135,"accuracy":0.6429},"high":{"n":76,"low":0.8903,"band":"high","high":0.9865,"agree":73,"accuracy":0.9605},"medium":{"n":104,"low":0.6795,"band":"medium","high":0.8397,"agree":80,"accuracy":0.7692}},"provider":"rule_based_v1+lex99e1195c7d6f","lexicon_id":"99e1195c7d6f","contract_published_at":"2026-10-23T12:25:29.022232+00:00","confidence_is_band":true,"confidence_is_probability":false,"decision_taxonomy":"two_way","decidable_fraction_two_way":0.8854,"decidable_fraction_two_way_low":0.8565,"decidable_fraction_two_way_high":0.9143,"decidable_fraction_three_way":0.8586,"decidable_fraction_three_way_low":0.8287,"decidable_fraction_three_way_high":0.8884,"decidable_population":5529,"validation_sample_size":438,"three_way_cost_fraction":0.0268,"three_way_cost_records":148,"decision_counts":{"automatable":230,"not_automatable":5299},"decision_split":{"automatable":{"count":230,"share":0.04159884246699222},"not_automatable":{"count":5299,"share":0.9584011575330078}},"diagnostic_work_type_counts":{"mixed":24,"physical":5275,"data_only":230}}}