{"data":{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38961904", "kind": "science-record", "revision": 2, "content_hash": "sha256:2083295a2f7076a961fdbfbcd6fb65682b4fff4bc6cba3cb52e695256af2fbae", "created_at": "2026-11-01T20:29:02Z", "updated_at": "2026-11-01T21:33:24Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Immunotherapy-relevance of a candidate prognostic score for Acute Myeloid Leukemia.", "doi": "10.1016/j.heliyon.2024.e32154", "pmid": "38961904", "authors": ["Yiyun Pan", "Wen Zeng", "Xiaoming Nie", "Hailong Chen", "Chuanhua Xie", "Shouju Guo", "Dechang Xu", "Yijian Chen"], "journal": "Heliyon", "year": 2024, "abstract": "Background: Acute Myeloid Leukemia (AML) exhibits a wide array of phenotypic manifestations, progression patterns, and heterogeneous responses to immunotherapies, suggesting involvement of complex immunobiological mechanisms. This investigation aimed to develop an integrated prognostic model for AML by incorporating cancer driver genes, along with clinical and phenotypic characteristics of the disease, and to assess its implications for immunotherapy responsiveness.\nMethods: Critical oncogenic driver genes linked to survival were identified by screening primary effector and corresponding gene pairs using data from The Cancer Genome Atlas (TCGA), through univariate Cox proportional hazard regression analysis. This was independently verified using dataset GSE37642. Primary effector genes were further refined using LASSO regression. Transcriptomic profiling was quantified using multivariate Cox regression, and the derived prognostic score was subsequently validated. Finally, a multivariate Cox regression model was developed, incorporating the transcriptomic score along with clinical parameters such as age, gender, and French-American-British (FAB) classification subtype. The 'Accurate Prediction Model of AML Overall Survival Score' (APMAO) was developed and subsequently validated. Investigations were conducted into functional pathway enrichment, alterations in the gene mutational landscape, and the extent of immune cell infiltration associated with varying APMAO scores. To further investigate the potential of APMAO scores as a predictive biomarker for responsiveness to cancer immunotherapy, we conducted a series of analyses. These included examining the expression profiles of genes related to immune checkpoints, the interferon-gamma signaling pathway, and m6A regulation. Additionally, we explored the relationship between these gene expression patterns and the Tumor Immune Dysfunction and Exclusion (TIDE) dysfunction scores.\nResults: Through the screening of 95 cancer genes associated with survival and 313 interacting gene pairs, seven genes (ACSL6, MAP3K1, CHIC2, HIP1, PTPN6, TFEB, and DAXX) were identified, leading to the derivation of a transcriptional score. Age and the transcriptional score were significant predictors in Cox regression analysis and were integral to the development of the final APMAO model, which exhibited an AUC greater than 0.75 and was successfully validated. Notable differences were observed in the distribution of the transcriptional score, age, cytogenetic risk categories, and French-American-British (FAB) classification between high and low APMAO groups. Samples with high APMAO scores demonstrated significantly higher mutation rates and pathway enrichments in NFKB, TNF, JAK-STAT, and NOTCH signaling. Additionally, variations in immune cell infiltration and immune checkpoint expression, activation of the interferon-γ pathway, and expression of m6A regulators were noted, including a negative correlation between CD160, m6A expression, and APMAO scores.\nConclusion: The combined APMAO score integrating transcriptional and clinical parameters demonstrated robust prognostic performance in predicting AML survival outcomes. It was linked to unique phenotypic characteristics, distinctive immune and mutational profiles, and patterns of expression for markers related to immunotherapy sensitivity. These observations suggest the potential for facilitating precision immunotherapy and advocate for its exploration in upcoming clinical trials.", "claims": [{"id": "claim-method", "statement": "To identify potential cancer driver genes associated with overall survival (OS), we performed univariate Cox proportional hazard regression analysis using the R packages survivor (version 3.2–7) and survminer (version 0.4.8).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38961904", "locator": "sections[4] \"Identification of cancer driver genes\" paragraph 2", "quote": "To identify potential cancer driver genes associated with overall survival (OS), we performed univariate Cox proportional hazard regression analysis using the R packages survivor (version 3.2–7) and survminer (version 0.4.8)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "GSE37642 (weblink [39]), GSE12417 (weblink: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE12417), GSE10358 (weblink: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE10358), GSE146173 (URL: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE146173), and GSE106291 (URL: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE106291) dataset were obtained from the Gene Expression Omnibus (GEO) database (URL: https://www.ncbi.nlm.nih.gov/geo/), while the TCGA-LAML data was acquired from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/). All the data generated or analyzed during the course of this study have been included in this published article and its accompanying supplementary information files.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38961904", "locator": "sections[15] \"Availability of data and materials\" paragraph 2", "quote": "GSE37642 (weblink [39]), GSE12417 (weblink: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE12417), GSE10358 (weblink: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE10358), GSE146173 (URL: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE146173), and GSE106291 (URL: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE106291) dataset were obtained from the Gene Expression Omnibus (GEO) database (URL: https://www.ncbi.nlm.nih.gov/geo/), while the TCGA-LAML data was acquired from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/). All the data generated or analyzed during the course of this study have been included in this published article and its accompanying supplementary information files."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The combined APMAO score integrating transcriptional and clinical parameters demonstrated robust prognostic performance in predicting AML survival outcomes.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38961904", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "The combined APMAO score integrating transcriptional and clinical parameters demonstrated robust prognostic performance in predicting AML survival outcomes."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The combined APMAO score integrating transcriptional and clinical parameters demonstrated robust prognostic performance in predicting AML survival outcomes.", "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:38961904"], "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:GSE10358"], "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:GSE10358"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38961904", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1016/j.heliyon.2024.e32154", "sha256": "sha256:fad73c96bc3ac43e1e733992312dacf34e68fad692216c49d4c90963057c5627", "bytes": 89317, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1016/j.heliyon.2024.e32154", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11219318", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE10358", "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=GSE10358", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE10358", "version": null, "note": "GSE37642 (weblink [39]), GSE12417 (weblink: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE12417), GSE10358 (weblink: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE10358), GSE146173 (URL: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE146173), and GSE106291 (URL: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE106291) dataset were obtained from the Gene Expression Omnibus (GEO) database (URL: https://www.ncbi.nlm.nih.gov/geo/), while the TCGA-LAML data was acquired from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/). All the data generated or analyzed during the course of this study have been included in this published article and its accompanying supplementary information files.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "GSE37642 (weblink [39]), GSE12417 (weblink: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE12417), GSE10358 (weblink: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE10358), GSE146173 (URL: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE146173), and GSE106291 (URL: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE106291) dataset were obtained from the Gene Expression Omnibus (GEO) database (URL: https://www.ncbi.nlm.nih.gov/geo/), while the TCGA-LAML data was acquired from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/). All the data generated or analyzed during the course of this study have been included in this published article and its accompanying supplementary information files.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM1226121", "GSM1254265", "GSM1254266", "GSM1254267", "GSM1254268", "GSM1254269", "GSM1254270", "GSM1254271", "GSM1254272", "GSM1254273", "GSM1254274", "GSM1254275", "GSM1254276", "GSM1254277", "GSM1254278", "GSM1254279", "GSM1254280", "GSM1254281", "GSM1254282", "GSM1254283", "GSM1254284", "GSM1254285", "GSM1254286", "GSM1254287", "GSM1254288", "GSM262030", "GSM262031", "GSM262032", "GSM262033", "GSM262034", "GSM262035", "GSM262036", "GSM262037", "GSM262038", "GSM262039", "GSM262040", "GSM262041", "GSM262042", "GSM262043", "GSM262044", "GSM262045", "GSM262046", "GSM262047", "GSM262048", "GSM262049", "GSM262050", "GSM262051", "GSM262052", "GSM262053", "GSM262054", "GSM262055", "GSM262056", "GSM262057", "GSM262058", "GSM262059", "GSM262060", 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"note": "GSE37642 (weblink [39]), GSE12417 (weblink: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE12417), GSE10358 (weblink: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE10358), GSE146173 (URL: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE146173), and GSE106291 (URL: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE106291) dataset were obtained from the Gene Expression Omnibus (GEO) database (URL: https://www.ncbi.nlm.nih.gov/geo/), while the TCGA-LAML data was acquired from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/). All the data generated or analyzed during the course of this study have been included in this published article and its accompanying supplementary information files.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "GSE37642 (weblink [39]), GSE12417 (weblink: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE12417), GSE10358 (weblink: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE10358), GSE146173 (URL: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE146173), and GSE106291 (URL: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE106291) dataset were obtained from the Gene Expression Omnibus (GEO) database (URL: https://www.ncbi.nlm.nih.gov/geo/), while the TCGA-LAML data was acquired from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/). All the data generated or analyzed during the course of this study have been included in this published article and its accompanying supplementary information files.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM2835244", "GSM2835245", "GSM2835246", "GSM2835247", "GSM2835248", "GSM2835249", "GSM2835250", "GSM2835251", "GSM2835252", "GSM2835253", "GSM2835254", "GSM2835255", "GSM2835256", "GSM2835257", "GSM2835258", "GSM2835259", "GSM2835260", "GSM2835261", "GSM2835262", "GSM2835263", "GSM2835264", "GSM2835265", "GSM2835266", "GSM2835267", "GSM2835268", "GSM2835269", "GSM2835270", "GSM2835271", "GSM2835272", "GSM2835273", "GSM2835274", "GSM2835275", "GSM2835276", "GSM2835277", "GSM2835278", "GSM2835279", "GSM2835280", "GSM2835281", "GSM2835282", "GSM2835283", "GSM2835284", "GSM2835285", "GSM2835286", "GSM2835287", "GSM2835288", "GSM2835289", "GSM2835290", "GSM2835291", "GSM2835292", "GSM2835293", "GSM2835294", "GSM2835295", "GSM2835296", 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was acquired from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/). 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"GSM924578", "GSM924579"], "sample_count": 984, "group_sizes": null, "group_sizes_basis": null, "platform": "570;96;97", "taxon": "Homo sapiens", "title": "Prognostic gene signature for AML", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE10358 not downloaded", "asset:data:geo:GSE10358 licence not read", "asset:data:geo:GSE106291 not downloaded", "asset:data:geo:GSE106291 licence not read", "asset:data:geo:GSE12417 not downloaded", "asset:data:geo:GSE12417 licence not read", "asset:data:geo:GSE146173 not downloaded", "asset:data:geo:GSE146173 licence not read", "asset:data:geo:GSE37642 not downloaded", "asset:data:geo:GSE37642 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": "To identify potential cancer driver genes associated with overall survival (OS), we performed univariate Cox proportional hazard regression analysis using the R packages survivor (version 3.2–7) and survminer (version 0.4.8)."}}, {"id": "method-2", "family": "network_analysis", "specific": "pathway_enrichment", "label": "Network / pathway analysis", "dsl_operations": ["pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.7, "band": "medium", "quote": "pathway enrichment"}}], "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": ["GSE37642", "GSE12417", "GSE10358", "GSE146173", "GSE106291"], "resolved_accessions": ["GSE37642", "GSE12417", "GSE10358", "GSE146173", "GSE106291"], "statements": ["GSE37642 (weblink [39]), GSE12417 (weblink: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE12417), GSE10358 (weblink: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE10358), GSE146173 (URL: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE146173), and GSE106291 (URL: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE106291) dataset were obtained from the Gene Expression Omnibus (GEO) database (URL: https://www.ncbi.nlm.nih.gov/geo/), while the TCGA-LAML data was acquired from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/). All the data generated or analyzed during the course of this study have been included in this published article and its accompanying supplementary information files."], "basis": "The paper names GSE37642, GSE12417, GSE10358, GSE146173, GSE106291 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:38961904", "data:geo:GSE10358"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE10358"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T19:23:04Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 21436, "completion_tokens": 2325, "cached_tokens": 264}}, "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:38961904", "type": "paper", "label": "pmid:38961904"}, {"id": "source:pmid:38961904", "type": "manuscript", "label": "PMID 38961904 full text"}, {"id": "data:geo:GSE10358", "type": "dataset", "label": "GEO GSE10358"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:38961904", "type": "paper", "label": "pmid:38961904"}, {"id": "source:pmid:38961904", "type": "manuscript", "label": "PMID 38961904 full text"}, {"id": "data:geo:GSE10358", "type": "dataset", "label": "GEO GSE10358"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-38961904", "type": "extraction", "used": ["source:pmid:38961904"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE10358", "type": "accession_resolution", "used": ["data:geo:GSE10358"], "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:38961904", "parent_hash": "sha256:e6b9918f7358128f281b73d4b893b197de55fb2c9ff6edf1fe0a07273c4d6a20", "fork_of": null, "generation": 1}, "gaps": ["asset:data:geo:GSE10358 licence not read", "asset:data:geo:GSE10358 not downloaded", "asset:data:geo:GSE106291 licence not read", "asset:data:geo:GSE106291 not downloaded", "asset:data:geo:GSE12417 licence not read", "asset:data:geo:GSE12417 not downloaded", "asset:data:geo:GSE146173 licence not read", "asset:data:geo:GSE146173 not downloaded", "asset:data:geo:GSE37642 licence not read", "asset:data:geo:GSE37642 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": ["To identify potential cancer driver genes associated with overall survival (OS), we performed univariate Cox proportional hazard regression analysis using the R packages survivor (version 3.2–7) and survminer (version 0.4.8)."], "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":"2026-11-04T03:48:53+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}}}