{"data":{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:28444881", "kind": "science-record", "revision": 7, "content_hash": "sha256:af84679d786d29231e88b8dfed9260e0aff93a87ddca3a61b4e6b083a10346eb", "created_at": "2026-11-01T15:40:25Z", "updated_at": "2026-11-01T21:20:53Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A three-lncRNA expression signature associated with the prognosis of gastric cancer patients.", "doi": "10.1002/cam4.1047", "pmid": "28444881", "authors": ["Peng Song", "Bo Jiang", "Zhijian Liu", "Jie Ding", "Song Liu", "Wenxian Guan"], "journal": "Cancer medicine", "year": 2017, "abstract": "Long noncoding RNAs (lncRNAs) have emerged as essential players in gene regulation. An ever-increasing number of lncRNAs has been found to be associated with the biogenesis and prognosis of gastric cancer (GC). We aimed to develop an lncRNA signature with prognostic value for survival outcomes of GC. Using an lncRNA mining approach, we analyzed the lncRNA expression profiles of 492 GC patients from the Gene Expression Omnibus (GEO), which consisted of the GSE62254 set (N = 300) and the GSE15459 set (N = 192). The associations between the lncRNAs' expression and survival outcome were evaluated. A set of three lncRNAs (LINC01140, TGFB2-OT1, and RP11-347C12.10) was identified to significantly correlate with overall survival. These lncRNAs were then combined to form a single prognostic signature. Based on this three-lncRNA expression signature, patients in the GSE62254 set were classified into high- and low-risk subgroups with significantly different overall survival (hazard ratio [HR] = 1.93, P < 0.001) and disease-free survival (HR = 1.91, P < 0.001). Good reproducibility for the prognostic value of this lncRNA signature was confirmed in the GSE15459 set. Further analysis showed that the prognostic value of this signature was independent of some clinical characteristics. Gene set enrichment analysis indicated that high-risk scores positively related to several molecular pathways of cancer metastasis. Our results suggest that this innovative lncRNA expression signature may be a useful biomarker for the prognosis of patients with GC based on bioinformatics analysis.", "claims": [{"id": "claim-method", "statement": "A multivariate stepwise Cox regression analysis was performed to evaluate the correlation between variables, including selected demographic characteristics and clinical features, risk scores (as continuous variables) and GC survival.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:28444881", "locator": "sections[13] \"Stepwise Cox regression model for survival\" paragraph 2", "quote": "A multivariate stepwise Cox regression analysis was performed to evaluate the correlation between variables, including selected demographic characteristics and clinical features, risk scores (as continuous variables) and GC survival."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The GC gene expression data used in this study were obtained from publicly available GEO databases. To evaluate the association between lncRNA expression signatures and GC survival, we selected the microarray expression profiles based on three criteria: (1) the profiles should be generated by the Affymetrix HG‐U133 Plus 2.0 Array (GPL570 platform), (2) the corresponding clinical data, such as histological classification and follow‐up information, were available online, and (3) the sample size was >100. This resulted in two sets (GSE62254 and GSE15459) that were screened in our study. Raw CEL files of the expression for the two GEO datasets were downloaded, and the Robust Multichip Average (RMA) algorithm was performed for background‐adjustment, quantile normalization, and log‐transformation by the R package “affy” 12.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:28444881", "locator": "sections[3] \"GC data sets\" paragraph 2", "quote": "The GC gene expression data used in this study were obtained from publicly available GEO databases."}, {"asset": "source:pmid:28444881", "locator": "sections[3] \"GC data sets\" paragraph 2", "quote": "To evaluate the association between lncRNA expression signatures and GC survival, we selected the microarray expression profiles based on three criteria: (1) the profiles should be generated by the Affymetrix HG‐U133 Plus 2.0 Array (GPL570 platform), (2) the corresponding clinical data, such as histological classification and follow‐up information, were available online, and (3) the sample size was >100."}, {"asset": "source:pmid:28444881", "locator": "sections[3] \"GC data sets\" paragraph 2", "quote": "This resulted in two sets (GSE62254 and GSE15459) that were screened in our study."}, {"asset": "source:pmid:28444881", "locator": "sections[4] \"Microarray expression processing and lncRNA profile mining\" paragraph 2", "quote": "Raw CEL files of the expression for the two GEO datasets were downloaded, and the Robust Multichip Average (RMA) algorithm was performed for background‐adjustment, quantile normalization, and log‐transformation by the R package “affy” 12."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Mining lncRNA expression profiles from the public GEO microarray datasets GSE62254 and GSE15459, with univariate Cox regression, random survival forests, and multivariable Cox regression, identified a three-lncRNA prognostic signature for gastric cancer.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:28444881", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Using an lncRNA mining approach, we analyzed the lncRNA expression profiles of 492 GC patients from the Gene Expression Omnibus (GEO), which consisted of the GSE62254 set (N = 300) and the GSE15459 set (N = 192)."}, {"asset": "source:pmid:28444881", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "A set of three lncRNAs (LINC01140, TGFB2‐OT1, and RP11‐347C12.10) was identified to significantly correlate with overall survival."}, {"asset": "source:pmid:28444881", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "These lncRNAs were then combined to form a single prognostic signature."}, {"asset": "source:pmid:28444881", "locator": "sections[9] \"Identification of prognostic lncRNAs\" paragraph 2", "quote": "After subjecting the ncRNA expression data to univariable Cox regression analysis by BRB‐Array Tools, we identified 85 and 21 ncRNAs that strongly correlated with the patients’ OS from the two sets, respectively."}, {"asset": "source:pmid:28444881", "locator": "sections[9] \"Identification of prognostic lncRNAs\" paragraph 2", "quote": "To make the model more practical, RSF were performed on the basis of the 11 lncRNAs, resulting in three lncRNAs remaining in the model."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Mining lncRNA expression profiles from the public GEO microarray datasets GSE62254 and GSE15459, with univariate Cox regression, random survival forests, and multivariable Cox regression, identified a three-lncRNA prognostic signature for gastric cancer.", "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:28444881"], "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. 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To evaluate the association between lncRNA expression signatures and GC survival, we selected the microarray expression profiles based on three criteria: (1) the profiles should be generated by the Affymetrix HG‐U133 Plus 2.0 Array (GPL570 platform), (2) the corresponding clinical data, such as histological classification and follow‐up information, were available online, and (3) the sample size was >100. This resulted in two sets (GSE62254 and GSE15459) that were screened in our study. Raw CEL files of the expression for the two GEO datasets were downloaded, and the Robust Multichip Average (RMA) algorithm was performed for background‐adjustment, quantile normalization, and log‐transformation by the R package “affy” 12.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The GC gene expression data used in this study were obtained from publicly available GEO databases. 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Raw CEL files of the expression for the two GEO datasets were downloaded, and the Robust Multichip Average (RMA) algorithm was performed for background‐adjustment, quantile normalization, and log‐transformation by the R package “affy” 12.", "access_statement_kind": "repository_route_statement", "sample_accessions": ["GSM387788", "GSM387789", "GSM387790", "GSM387791", "GSM387792", "GSM387793", "GSM387794", "GSM387795", "GSM387796", "GSM387797", "GSM387798", "GSM387799", "GSM387800", "GSM387801", "GSM387802", "GSM387803", "GSM387804", "GSM387805", "GSM387806", "GSM387807", "GSM387808", "GSM387809", "GSM387810", "GSM387811", "GSM387812", "GSM387813", "GSM387814", "GSM387815", "GSM387816", "GSM387817", "GSM387818", "GSM387819", "GSM387820", "GSM387821", "GSM387822", "GSM387823", "GSM387824", "GSM387825", "GSM387826", "GSM387827", "GSM387828", "GSM387829", "GSM387830", "GSM387831", "GSM387832", "GSM387833", "GSM387834", "GSM387835", "GSM387836", "GSM387837", "GSM387838", "GSM387839", "GSM387840", "GSM387841", "GSM387842", "GSM387843", "GSM387844", "GSM387845", "GSM387846", "GSM387847", "GSM387848", "GSM387849", "GSM387850", "GSM387851", "GSM387852", "GSM387853", "GSM387854", "GSM387855", "GSM387856", "GSM387857", "GSM387858", "GSM387859", "GSM387860", "GSM387861", "GSM387862", "GSM387863", "GSM387864", "GSM387865", "GSM387866", "GSM387867", "GSM387868", "GSM387869", "GSM387870", "GSM387871", "GSM387872", "GSM387873", "GSM387874", "GSM387875", "GSM387876", "GSM387877", "GSM387878", "GSM387879", "GSM387880", "GSM387881", "GSM387882", "GSM387883", "GSM387884", "GSM387885", "GSM387886", "GSM387887", "GSM387888", "GSM387889", "GSM387890", "GSM387891", "GSM387892", "GSM387893", "GSM387894", "GSM387895", "GSM387896", "GSM387897", "GSM387898", "GSM387899", "GSM387900", "GSM387901", "GSM387902", "GSM387903", "GSM387904", "GSM387905", "GSM387906", "GSM387907", "GSM387908", "GSM387909", "GSM387910", "GSM387911", "GSM387912", "GSM387913", "GSM387914", "GSM387915", "GSM387916", "GSM387917", "GSM387918", "GSM387919", "GSM387920", "GSM387921", "GSM387922", "GSM387923", "GSM387924", "GSM387925", "GSM387926", "GSM387927", "GSM387928", "GSM387929", "GSM387930", "GSM387931", "GSM387932", "GSM387933", "GSM387934", "GSM387935", "GSM387936", "GSM387937", "GSM387938", "GSM387939", "GSM387940", "GSM387941", "GSM387942", "GSM387943", "GSM387944", "GSM387945", "GSM387946", "GSM387947", "GSM387948", "GSM387949", "GSM387950", "GSM387951", "GSM387952", "GSM387953", "GSM387954", "GSM387955", "GSM387956", "GSM387957", "GSM387958", "GSM387959", "GSM387960", "GSM387961", "GSM387962", "GSM387963", "GSM387964", "GSM387965", "GSM387966", "GSM387967", "GSM387968", "GSM387969", "GSM387970", "GSM387971", "GSM387972", "GSM387973", "GSM387974", "GSM387975", "GSM387976", "GSM387977", "GSM387978", "GSM387979", "GSM387980", "GSM387981", "GSM387982", "GSM387983", "GSM387984", "GSM387985", "GSM387986", "GSM387987"], "sample_count": 200, "group_sizes": null, "group_sizes_basis": null, "platform": "570", "taxon": "Homo sapiens", "title": "Gastric Cancer Project '08 (Singapore Patient Cohort)", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE62254", "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=GSE62254", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE62254", "version": null, "note": "The GC gene expression data used in this study were obtained from publicly available GEO databases. 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three‐lncRNA signature and patients’ clinicopathological features", "rows": [["Variables", "No. of patients", "Three‐lncRNA expression", "Three‐lncRNA score"], ["Low n (%)", "High n (%)", "P valuea", "Mean (SD)", "Median (IQR)", "P valueb"], ["GSE62254"], ["Age", "", "", "", "0.049", "", "", "0.043"], ["≤64 years", "161", "72 (44.7)", "89 (55.3)", "", "0.28 (0.53)", "0.18 (−0.08–0.54)", ""], [">64 years", "139", "78 (56.1)", "61 (43.9)", "", "0.16 (0.46)", "0.10 (−0.13–0.36)", ""], ["Sex", "", "", "", "0.714", "", "", "0.160"], ["Female", "101", "49 (48.5)", "52 (51.5)", "", "0.30 (0.57)", "0.16 (−0.02–0.55)", ""], ["Male", "199", "101 (50.8)", "98 (49.2)", "", "0.19 (0.46)", "0.15 (−0.13–0.41)", ""], ["Lauren", "", "", "", "<0.001", "", "", "<0.001"], ["Intestinal", "146", "96 (65.8)", "50 (34.2)", "", "0.05 (0.37)", "0.03 (−0.20–0.20)", ""], ["Diffuse", "134", "45 (33.6)", "89 (66.4)", "", "0.42 (0.56)", "0.27 (0.06–0.69)", ""], ["Location", "", "", "", "1.000", "", "", "0.655"], 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"−0.51 (−1.89–0.39)", ""], ["Diffuse", "75", "26 (34.7)", "49 (65.3)", "", "1.06 (1.83)", "0.86 (−0.47–2.61)", ""], ["TNM", "", "", "", "0.639", "", "", "0.431"], ["I/II", "60", "34 (56.7)", "26 (43.3)", "", "−0.05 (2.38)", "−0.27 (−1.62–1.50)", ""], ["III/IV", "132", "70 (53.0)", "62 (47.0)", "", "0.13 (1.88)", "−0.02 (−1.00–1.30)", ""]], "row_count": 31, "column_count": 8, "numeric_cells": 114, "header_rows": 2, "locator": "section[12]/table-wrap[1]#cam41047-tbl-0003"}}, {"id": "asset:table:pmid:28444881:4", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:76a98f79d6af296f497c7d2168b8acb10426a80ceab544eac3a32f7a778680ea", "bytes": 835, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[13]/table-wrap[1]#cam41047-tbl-0004", "version": null, "note": "Results of stepwise Cox regression analysis on gastric cancer patients’ survival", "structure": {"pmcid": "PMC5463065", "section_index": 13, "section_title": "Stepwise Cox regression model for survival", "row_count": 12, "column_count": 6}, "table": {"label": "Table 4", "caption": "Results of stepwise Cox regression analysis on gastric cancer patients’ survival", "rows": [["Final variables", "β", "SE", "HR", "95% CI", "P value"], ["GSE62254 (OS)"], ["Age", "0.03", "0.01", "1.03", "1.01–1.05", "0.001"], ["TNM", "0.78", "0.11", "2.19", "1.77–2.72", "<0.001"], ["Three‐lncRNA risk score", "0.89", "0.16", "2.42", "1.79–3.28", "<0.001"], ["GSE62254 (DFS)"], ["Age", "0.02", "0.01", "1.02", "1.00–1.035", "0.029"], ["TNM", "0.83", "0.12", "2.28", "1.82–2.87", "<0.001"], ["Three‐lncRNA risk score", "0.69", "0.16", "2.00", "1.47–2.72", "<0.001"], ["GSE15459 (OS)"], ["TNM", "1.04", "0.14", "2.82", "2.16–3.68", "<0.001"], ["Three‐lncRNA risk score", "0.15", "0.05", "1.16", "1.04–1.28", "0.005"]], "row_count": 12, "column_count": 6, "numeric_cells": 44, "header_rows": 1, "locator": "section[13]/table-wrap[1]#cam41047-tbl-0004"}}], "gaps": ["asset:data:geo:GSE15459 not downloaded", "asset:data:geo:GSE15459 licence not read", "asset:data:geo:GSE62254 not downloaded", "asset:data:geo:GSE62254 licence not read"]}, "analysis": {"state": "complete", "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": "A multivariate stepwise Cox regression analysis was performed to evaluate the correlation between variables, including selected demographic characteristics and clinical features, risk scores (as continuous variables) and GC survival."}}], "targets": [], "gaps": []}, "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": ["GSE62254", "GSE15459"], "resolved_accessions": ["GSE62254", "GSE15459"], "statements": ["The GC gene expression data used in this study were obtained from publicly available GEO databases. To evaluate the association between lncRNA expression signatures and GC survival, we selected the microarray expression profiles based on three criteria: (1) the profiles should be generated by the Affymetrix HG‐U133 Plus 2.0 Array (GPL570 platform), (2) the corresponding clinical data, such as histological classification and follow‐up information, were available online, and (3) the sample size was >100. This resulted in two sets (GSE62254 and GSE15459) that were screened in our study. Raw CEL files of the expression for the two GEO datasets were downloaded, and the Robust Multichip Average (RMA) algorithm was performed for background‐adjustment, quantile normalization, and log‐transformation by the R package “affy” 12."], "basis": "The paper names GSE62254, GSE15459 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:28444881", "data:geo:GSE15459", "asset:table:pmid:28444881:1", "asset:table:pmid:28444881:2", "asset:table:pmid:28444881:3", "asset:table:pmid:28444881:4"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE15459"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-10-31T18:30:56Z", "independence": "same_provider"}, "evidence_check": {"method": "verbatim", "availability": null}, "calls": 8, "usage": {"prompt_tokens": 61187, "completion_tokens": 4018, "cached_tokens": 1840}}, "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:28444881", "type": "paper", "label": "pmid:28444881"}, {"id": "source:pmid:28444881", "type": "manuscript", "label": "PMID 28444881 full text"}, {"id": "data:geo:GSE15459", "type": "dataset", "label": "GEO GSE15459"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:28444881", "type": "paper", "label": "pmid:28444881"}, {"id": "source:pmid:28444881", "type": "manuscript", "label": "PMID 28444881 full text"}, {"id": "data:geo:GSE15459", "type": "dataset", "label": "GEO GSE15459"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-28444881", "type": "extraction", "used": ["source:pmid:28444881"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE15459", "type": "accession_resolution", "used": ["data:geo:GSE15459"], "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:28444881", "parent_hash": "sha256:62219fd99a0df5bbf99b5a6c6956df8c673375452f4fddf06ee20f8cd79f0b99", "fork_of": null, "generation": 6}, "gaps": ["asset:data:geo:GSE15459 licence not read", "asset:data:geo:GSE15459 not downloaded", "asset:data:geo:GSE62254 licence not read", "asset:data:geo:GSE62254 not downloaded", "execution.not_run", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"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": ["A multivariate stepwise Cox regression analysis was performed to evaluate the correlation between variables, including selected demographic characteristics and clinical features, risk scores (as continuous variables) and GC survival."], "order_basis": "analysis_only", "id": "assay:1"}]},"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":"2026-11-04T02:21:30+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}}}