{"data":{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34413861", "kind": "science-record", "revision": 6, "content_hash": "sha256:229d3aefe290da62988d27089318112af1784aafeee8e1acdb7301ac53a2cb14", "created_at": "2026-11-01T15:41:48Z", "updated_at": "2026-11-01T21:26:02Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Comprehensive Characterization of RNA Processing Factors in Gastric Cancer Identifies a Prognostic Signature for Predicting Clinical Outcomes and Therapeutic Responses.", "doi": "10.3389/fimmu.2021.719628", "pmid": "34413861", "authors": ["Shenghan Lou", "Fanzheng Meng", "Xin Yin", "Yao Zhang", "Bangling Han", "Yingwei Xue"], "journal": "Frontiers in immunology", "year": 2021, "abstract": "RNA processing converts primary transcript RNA into mature RNA. Altered RNA processing drives tumor initiation and maintenance, and may generate novel therapeutic opportunities. However, the role of RNA processing factors in gastric cancer (GC) has not been clearly elucidated. This study presents a comprehensive analysis exploring the clinical, molecular, immune, and drug response features underlying the RNA processing factors in GC. This study included 1079 GC cases from The Cancer Genome Atlas (TCGA, training set), our hospital cohort, and two other external validation sets (GSE15459, GSE62254). We developed an RNA processing-related prognostic signature using Cox regression with the least absolute shrinkage and selection operator (LASSO) penalty. The prognostic value of the signature was evaluated using a multiple-method approach. The genetic variants, pathway activation, immune heterogeneity, drug response, and splicing features associated with the risk signature were explored using bioinformatics methods. Among the tested 819 RNA processing genes, we identified five distinct RNA processing patterns with specific clinical outcomes and biological features. A 10-gene RNA processing-related prognostic signature, involving ZBTB7A, METTL2B, CACTIN, TRUB2, POLDIP3, TSEN54, SUGP1, RBMS1, TGFB1, and PWP2, was further identified. The signature was a powerful and robust prognosis factor in both the training and validation datasets. Notably, it could stratify the survival of patients with GC in specific tumor-node-metastasis (TNM) classification subgroups. We constructed a composite prognostic nomogram to facilitate clinical practice by integrating this signature with other clinical variables (TNM stage, age). Patients with low-risk scores were characterized with good clinical outcomes, proliferation, and metabolism hallmarks. Conversely, poor clinical outcome, invasion, and metastasis hallmarks were enriched in the high-risk group. The RNA processing signature was also involved in tumor microenvironment reprogramming and regulating alternative splicing, causing different drug response features between the two risk groups. The low-risk subgroup was characterized by high genomic instability, high alternative splicing and might benefit from the immunotherapy. Our findings highlight the prognostic value of RNA processing factors for patients with GC and provide insights into the specific clinical and molecular features underlying the RNA processing-related signature, which may be important for patient management and targeting treatment.", "claims": [{"id": "claim-method", "statement": "Univariate Cox proportional hazards regression analysis was first performed on the expression matrix of RNA processing factors to estimate the relationship between RNA processing factors and prognosis (OS) in the TCGA-STAD cohort. RNA processing factors with p-value < 0.1 were selected as the potential prognosis-related RNA processing factors. The least absolute shrinkage and selection operator (LASSO) penalty was performed in the discovery cohort to build an optimal prognostic signature with the minimum number of RNA processing factors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34413861", "locator": "sections[7] \"Identification of the RNA Processing-Related Prognostic Signature\" paragraph 2", "quote": "Univariate Cox proportional hazards regression analysis was first performed on the expression matrix of RNA processing factors to estimate the relationship between RNA processing factors and prognosis (OS) in the TCGA-STAD cohort."}, {"asset": "source:pmid:34413861", "locator": "sections[7] \"Identification of the RNA Processing-Related Prognostic Signature\" paragraph 2", "quote": "RNA processing factors with p-value < 0.1 were selected as the potential prognosis-related RNA processing factors."}, {"asset": "source:pmid:34413861", "locator": "sections[7] \"Identification of the RNA Processing-Related Prognostic Signature\" paragraph 3", "quote": "The least absolute shrinkage and selection operator (LASSO) penalty was performed in the discovery cohort to build an optimal prognostic signature with the minimum number of RNA processing factors."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34413861", "locator": "sections[24] \"Data Availability Statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. 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signature.", "structure": {"pmcid": "PMC8369824", "section_index": 7, "section_title": "Identification of the RNA Processing-Related Prognostic Signature", "row_count": 14, "column_count": 6}, "table": {"label": "Table 2", "caption": "Univariate and multivariate Cox analyses of the RNA processing-related signature.", "rows": [["Dataset", "Factor", "Univariate", "Multivariate"], ["HR (95% CI)", "p-value", "HR (95% CI)", "p-value"], ["TCGA cohort (n = 373)", "Risk score (increasing values)", "9.280 (4.746, 18.148)", "<0.001", "9.918 (4.926, 19.968)", "<0.001"], ["Age (increasing years)", "1.021 (1.005, 1.037)", "0.012", "1.027 (1.010, 1.045)", "0.002"], ["Sex (male vs. female)", "1.333 (0.936, 1.899)", "0.112", "", ""], ["TNM stage (III + IV vs. I + II)", "1.891 (1.325, 2.698)", "<0.001", "2.101 (1.464, 3.015)", "<0.001"], ["HMU cohort (n = 214)", "Risk score (increasing values)", "2.819 (1.041, 7.637)", "0.041", "2.819 (1.041, 7.637)", "0.041"], ["Age (increasing years)", "1.009 (0.992, 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[["Dataset", "Factor", "Univariate", "Multivariate"], ["HR (95% CI)", "p-value", "HR (95% CI)", "p-value"], ["TCGA cohort (n = 373)", "Risk score (increasing values)", "9.280 (4.746, 18.148)", "<0.001", "9.918 (4.926, 19.968)", "<0.001"], ["Age (increasing years)", "1.021 (1.005, 1.037)", "0.012", "1.027 (1.010, 1.045)", "0.002"], ["Sex (male vs. female)", "1.333 (0.936, 1.899)", "0.112", "", ""], ["TNM stage (III + IV vs. I + II)", "1.891 (1.325, 2.698)", "<0.001", "2.101 (1.464, 3.015)", "<0.001"], ["HMU cohort (n = 214)", "Risk score (increasing values)", "2.819 (1.041, 7.637)", "0.041", "2.819 (1.041, 7.637)", "0.041"], ["Age (increasing years)", "1.009 (0.992, 1.027)", "0.284", "", ""], ["Sex (male vs. female)", "0.984 (0.615, 1.574)", "0.947", "", ""], ["TNM stage (III + IV vs. I + II)", "1.165 (0.710, 1.911)", "0.545", "", ""], ["GEO cohort (n = 492)", "Risk score (increasing values)", "3.601 (2.002, 6.474)", "<0.001", "1.632 (0.879, 3.029)", "0.121"], ["Age (increasing years)", "1.006 (0.995, 1.018)", "0.254", "", ""], ["Sex (male vs. female)", "1.056 (0.810, 1.377)", "0.686", "", ""], ["TNM stage (III + IV vs. I + II)", "4.245 (3.097, 5.817)", "<0.001", "3.983 (2.878, 5.511)", "<0.001"]], "row_count": 14, "column_count": 6, "numeric_cells": 41, "header_rows": 2, "locator": "section[16]/table-wrap[2]#T2"}}, {"id": "asset:table:pmid:34413861:6", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:b624286a69f5767855386635004d806a1601c48cdcabb6e77395833a3784883a", "bytes": 1681, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[16]/table-wrap[3]#T3", "version": null, "note": "Subgroup analysis of the RNA processing-related signature.", "structure": {"pmcid": "PMC8369824", "section_index": 16, "section_title": "Identification of the RNA Processing-Related Prognostic Signature", "row_count": 29, "column_count": 6}, "table": {"label": "Table 3", "caption": "Subgroup analysis of the RNA processing-related signature.", "rows": [["Data set", "Factor", "Subgroup analysis", "p-value for interaction"], ["Samples", "HR (95% CI)", "p-value"], ["TCGA cohort (n = 373)", "Sex", "", "", "", ""], ["Female", "133.000", "12.071 (3.737, 38.996)", "<0.001", "0.651"], ["Male", "240.000", "8.271 (3.611, 18.945)", "<0.001", ""], ["Age", "", "", "", ""], ["≤60", "120.000", "18.021 (4.949, 65.616)", "<0.001", "0.268"], ["> 60", "249.000", "7.543 (3.498, 16.267)", "<0.001", ""], ["Stage", "", "", "", ""], ["Early (I and II)", "164.000", "7.482 (2.349, 23.831)", "0.001", "0.660"], ["Advanced (III and IV)", "186.000", "11.097 (4.527, 27.202)", "<0.001", ""], ["HMU cohort (n = 214)", "Sex", "", "", "", ""], ["Female", "77.000", "2.162 (0.315, 14.849)", "0.433", "0.666"], ["Male", "136.000", "3.372 (1.037, 10.971)", "0.043", ""], ["Age", "", "", "", ""], ["≤60", "118.000", "2.369 (0.563, 9.977)", "0.240", "0.691"], ["> 60", "96.000", "3.509 (0.883, 13.943)", "0.075", ""], ["Stage", "", "", "", ""], ["Early (I and II)", "67.000", "1.243 (0.213, 7.264)", "0.809", "0.260"], ["Advanced (III and IV)", "147.000", "4.149 (1.234, 13.947)", "0.021", ""], ["GEO cohort (n = 492)", "Sex", "", "", "", ""], ["Female", "168.000", "6.633 (2.354, 18.691)", "<0.001", "0.113"], ["Male", "324.000", "2.651 (1.290, 5.447)", "0.008", ""], ["Age", "", "", "", ""], ["≤60", "178.000", "8.792 (3.039, 25.438)", "<0.001", "0.065"], ["> 60", "314.000", "2.583 (1.260, 5.291)", "0.010", ""], ["Stage", "", "", "", ""], ["Early (I and II)", "187.000", "2.796 (0.784, 9.976)", "0.113", "0.312"], ["Advanced (III and IV)", "305.000", "1.331 (0.654, 2.708)", "0.431", ""]], "row_count": 29, "column_count": 6, "numeric_cells": 73, "header_rows": 2, "locator": "section[16]/table-wrap[3]#T3"}}], "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", "asset:data:geo:PRJNA718168 not downloaded", "asset:data:geo:PRJNA718168 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": "reordered", "quote": "Univariate Cox proportional hazards regression analysis was first performed on the expression matrix of RNA processing factors to estimate the relationship between RNA processing factors and prognosis (OS) in the TCGA-STAD cohort. RNA processing factors with p-value < 0.1 were selected as the potential prognosis-related RNA processing factors. The least absolute shrinkage and selection operator (LASSO) penalty was performed in the discovery cohort to build an optimal prognostic signature with the minimum number of RNA processing factors."}}, {"id": "method-2", "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-3", "family": "clustering", "specific": "hierarchical", "label": "Clustering", "dsl_operations": ["hierarchical_cluster"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["feature_matrix"], "evidence": {"score": 0.8, "band": "high", "quote": "dendrogram"}}], "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": ["PRJNA718168", "GSE15459", "GSE62254"], "resolved_accessions": ["GSE15459", "GSE62254"], "statements": ["The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material."], "basis": "The paper names GSE15459, GSE62254 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:34413861", "data:geo:GSE15459", "asset:table:pmid:34413861:1", "asset:table:pmid:34413861:2", "asset:table:pmid:34413861:3", "asset:table:pmid:34413861:4", "asset:table:pmid:34413861:5", "asset:table:pmid:34413861:6"], "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": "2026-10-31T18:33:49Z", "independence": "single_reader"}, "evidence_check": {"method": "reordered", "availability": "verbatim"}, "calls": 3, "usage": {"prompt_tokens": 38537, "completion_tokens": 3261, "cached_tokens": 784}}, "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:34413861", "type": "paper", "label": "pmid:34413861"}, {"id": "source:pmid:34413861", "type": "manuscript", "label": "PMID 34413861 full text"}, {"id": "data:geo:GSE15459", "type": "dataset", "label": "GEO GSE15459"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:34413861", "type": "paper", "label": "pmid:34413861"}, {"id": "source:pmid:34413861", "type": "manuscript", "label": "PMID 34413861 full text"}, {"id": "data:geo:GSE15459", "type": "dataset", "label": "GEO GSE15459"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-34413861", "type": "extraction", "used": ["source:pmid:34413861"], "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:34413861", "parent_hash": "sha256:5ed011af9dd44cbfb9ef80d644ba1e82a96ae5f2b78ec39adbe1f9f34166d161", "fork_of": null, "generation": 5}, "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", "asset:data:geo:PRJNA718168 licence not read", "asset:data:geo:PRJNA718168 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": ["Univariate Cox proportional hazards regression analysis was first performed on the expression matrix of RNA processing factors to estimate the relationship between RNA processing factors and prognosis (OS) in the TCGA-STAD cohort. RNA processing factors with p-value < 0.1 were selected as the potential prognosis-related RNA processing factors. The least absolute shrinkage and selection operator (LASSO) penalty was performed in the discovery cohort to build an optimal prognostic signature with the minimum number of RNA processing factors."], "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":"2026-11-04T00:42:44+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}}}