{"data":{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39445158", "kind": "science-record", "revision": 2, "content_hash": "sha256:50e701da1b658cc8c579c655529869b3aa838c6ba11caa4b7e9b130f0b31f7ee", "created_at": "2026-11-01T20:29:42Z", "updated_at": "2026-11-01T21:34:13Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of potential therapeutic targets for systemic lupus erythematosus based on GEO database analysis and Mendelian randomization analysis.", "doi": "10.3389/fgene.2024.1454486", "pmid": "39445158", "authors": ["Aishanjiang Apaer", "Yanyan Shi", "Alimijiang Aobulitalifu", "Fujie Wen", "Adalaiti Muhetaer", "Nuermaimaiti Ajimu", "Maierhaba Sulitan", "Lei Cheng"], "journal": "Frontiers in genetics", "year": 2024, "abstract": "Background: Systemic lupus erythematosus (SLE) is a complex autoimmune disease. Current treatments mainly rely on immunosuppressants, which lack specificity and pose challenges during treatment. This study aims to deeply explore the molecular pathogenic mechanism of SLE through gene expression databases (GEO) and bioinformatics analysis methods, combined with Mendelian randomization analysis, to provide key clues for new therapeutic targets.\nMethods: In this study, the SLE-related gene chip dataset GSE65391 was selected from the GEO database, and the data were preprocessed and statistically analyzed using R language and bioinformatics tools. Differential expression analysis, weighted gene co-expression network analysis (WGCNA), GO, and KEGG enrichment analysis were used to screen differentially expressed genes (DEGs) for functional annotation and pathway localization. Furthermore, Mendelian randomization analysis was conducted to identify core genes closely related to SLE risk, and immune cell infiltration analysis and compound molecular docking studies were performed on the core gene ISG15.\nResults: The study successfully screened 3,456 DEGs and identified core gene modules highly related to SLE through WGCNA analysis, including key genes closely related to the pathogenesis of SLE, such as STAT1, DDX58, ISG15, IRF7, and IFIH1. In particular, this study found a significant positive correlation between the ISG15 gene and SLE, suggesting that it may be a potential risk factor for SLE. Additionally, through molecular docking technology, it was discovered that the ISG15 gene can effectively bind to two compounds, genistein, and flavopiridol, which have anti-inflammatory and immunosuppressive effects, respectively. This provides new potential drug targets for SLE treatment.\nDiscussion: As an immunomodulatory cytokine, ISG15 plays a crucial role in the pathogenesis of SLE. This study found that variations in the ISG15 gene may increase the risk of SLE and exacerbate inflammatory responses and tissue damage through multiple mechanisms. Furthermore, molecular docking revealed that genistein and flavopiridol can effectively bind to ISG15, offering a new approach for SLE treatment. These two compounds, with their anti-inflammatory and immunosuppressive properties, have the potential to slow the progression of SLE by influencing the expression and function of ISG15.\nConclusion: Through comprehensive bioinformatics analysis and Mendelian randomization analysis, this study deeply explored the molecular pathogenic mechanism of SLE and successfully identified ISG15 as a potential therapeutic target for SLE. Simultaneously, molecular docking technology revealed that two compounds, genistein and flavopiridol, have potential therapeutic effects with ISG15, providing new potential drugs for SLE treatment. These discoveries not only enhance our understanding of the pathogenesis of SLE but also provide important clues for developing new treatment strategies.", "claims": [{"id": "claim-method", "statement": "We employed a two-sample Mendelian randomization (MR) analysis to investigate the causal relationship between characteristic genes and the risk of systemic lupus erythematosus (SLE), using single nucleotide polymorphisms (SNPs) as instrumental variables.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39445158", "locator": "sections[10] \"MR analysis between signature genes and SLE\" paragraph 2", "quote": "We employed a two-sample Mendelian randomization (MR) analysis to investigate the causal relationship between characteristic genes and the risk of systemic lupus erythematosus (SLE), using single nucleotide polymorphisms (SNPs) as instrumental variables."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated and analyzed in this study are available in the GEO data repository (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65391): GSE65391. Additionally, the datasets used for Mendelian randomization in this study can be found in the IEU Open GWAS Project repository, with the exposure ID being prot-a-1573 (https://gwas.mrcieu.ac.uk/datasets/prot-a-1573/) and the outcome ID for systemic lupus erythematosus (SLE) being finn-b-M13_SLE (https://gwas.mrcieu.ac.uk/datasets/finn-b-M13_SLE/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39445158", "locator": "sections[25] \"Data availability statement\" paragraph 2", "quote": "The datasets generated and analyzed in this study are available in the GEO data repository (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65391): GSE65391. Additionally, the datasets used for Mendelian randomization in this study can be found in the IEU Open GWAS Project repository, with the exposure ID being prot-a-1573 (https://gwas.mrcieu.ac.uk/datasets/prot-a-1573/) and the outcome ID for systemic lupus erythematosus (SLE) being finn-b-M13_SLE (https://gwas.mrcieu.ac.uk/datasets/finn-b-M13_SLE/)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "", "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:39445158"], "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:GSE65391"], "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:GSE65391"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39445158", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fgene.2024.1454486", "sha256": "sha256:71ad840e51c0fbe2209ce9bfc9ff137cc73001a12c76ccb8b23bbfe65e9cd677", "bytes": 32952, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fgene.2024.1454486", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11496559", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE65391", "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=GSE65391", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE65391", "version": null, "note": "The datasets generated and analyzed in this study are available in the GEO data repository (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65391): GSE65391. Additionally, the datasets used for Mendelian randomization in this study can be found in the IEU Open GWAS Project repository, with the exposure ID being prot-a-1573 (https://gwas.mrcieu.ac.uk/datasets/prot-a-1573/) and the outcome ID for systemic lupus erythematosus (SLE) being finn-b-M13_SLE (https://gwas.mrcieu.ac.uk/datasets/finn-b-M13_SLE/).", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets generated and analyzed in this study are available in the GEO data repository (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65391): GSE65391. Additionally, the datasets used for Mendelian randomization in this study can be found in the IEU Open GWAS Project repository, with the exposure ID being prot-a-1573 (https://gwas.mrcieu.ac.uk/datasets/prot-a-1573/) and the outcome ID for systemic lupus erythematosus (SLE) being finn-b-M13_SLE (https://gwas.mrcieu.ac.uk/datasets/finn-b-M13_SLE/).", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM1594219", "GSM1594220", "GSM1594221", "GSM1594222", "GSM1594223", "GSM1594224", "GSM1594225", "GSM1594226", "GSM1594227", "GSM1594228", "GSM1594229", "GSM1594230", "GSM1594231", "GSM1594232", "GSM1594233", "GSM1594234", "GSM1594235", "GSM1594236", "GSM1594237", "GSM1594238", "GSM1594239", "GSM1594240", "GSM1594241", "GSM1594244", "GSM1594247", "GSM1594250", "GSM1594253", "GSM1594254", "GSM1594255", "GSM1594256", "GSM1594257", "GSM1594258", "GSM1594259", "GSM1594260", "GSM1594261", "GSM1594262", "GSM1594263", "GSM1594264", "GSM1594265", "GSM1594266", 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"GSM1595119", "GSM1595120", "GSM1595121", "GSM1595122", "GSM1595123", "GSM1595124", "GSM1595125", "GSM1595126", "GSM1595127", "GSM1595128", "GSM1595129", "GSM1595130", "GSM1595131", "GSM1595132", "GSM1595133", "GSM1595134", "GSM1595135", "GSM1595136", "GSM1595137", "GSM1595138", "GSM1595139", "GSM1595140", "GSM1595141", "GSM1595142", "GSM1595143", "GSM1595144", "GSM1595145", "GSM1595146", "GSM1595147", "GSM1595148", "GSM1595149", "GSM1595150", "GSM1595151", "GSM1595152", "GSM1595153", "GSM1595154", "GSM1595155", "GSM1595156", "GSM1595157", "GSM1595158", "GSM1595159", "GSM1595160", "GSM1595161", "GSM1595162", "GSM1595163", "GSM1595164", "GSM1595165", "GSM1595166", "GSM1595167", "GSM1595168", "GSM1595169", "GSM1595170", "GSM1595171", "GSM1595172", "GSM1595173", "GSM1595174", "GSM1595175", "GSM1595176", "GSM1595177", "GSM1595178", "GSM1595179", "GSM1595180", "GSM1595181", "GSM1595182", "GSM1595183", "GSM1595184", "GSM1595185", "GSM1595186", "GSM1595187", "GSM1595188", "GSM1595189", "GSM1595190", "GSM1595191", "GSM1595192", "GSM1595193", "GSM1595194", "GSM1595195", "GSM1595196", "GSM1595197", "GSM1595198", "GSM1595199", "GSM1595200", "GSM1595201", "GSM1595202", "GSM1595203", "GSM1595204", "GSM1595205", "GSM1595206", "GSM1595207", "GSM1595208", "GSM1595209", "GSM1595210", "GSM1595211", "GSM1595212", "GSM1595213", "GSM1595214", "GSM1595215", "GSM1595216", "GSM1595217", "GSM1595218", "GSM1595219", "GSM1595220", "GSM1595221", "GSM1595222"], "sample_count": 996, "group_sizes": null, "group_sizes_basis": null, "platform": "10558", "taxon": "Homo sapiens", "title": "Longitudinal transcriptional pediatric SLE study with clinical parameters", "resolution_checked_at": "2026-11-01T15:39:55Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE65391 not downloaded", "asset:data:geo:GSE65391 licence not read"]}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "family": "genetic_association", "specific": "mendelian_randomisation", "label": "Genetic association (GWAS / linkage / MR)", "dsl_operations": ["mr_estimate"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["genotype_phenotype_table"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "We employed a two-sample Mendelian randomization (MR) analysis to investigate the causal relationship between characteristic genes and the risk of systemic lupus erythematosus (SLE), using single nucleotide polymorphisms (SNPs) as instrumental variables."}}, {"id": "method-2", "family": "genetic_association", "specific": "gwas", "label": "Genetic association (GWAS / linkage / MR)", "dsl_operations": ["gwas_locus_scan"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["genotype_phenotype_table"], "evidence": {"score": 0.8, "band": "high", "quote": "GWAS"}}], "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": ["mr_estimate"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: mendelian_randomisation."}, "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": ["GSE65391"], "resolved_accessions": ["GSE65391"], "statements": ["The datasets generated and analyzed in this study are available in the GEO data repository (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65391): GSE65391. Additionally, the datasets used for Mendelian randomization in this study can be found in the IEU Open GWAS Project repository, with the exposure ID being prot-a-1573 (https://gwas.mrcieu.ac.uk/datasets/prot-a-1573/) and the outcome ID for systemic lupus erythematosus (SLE) being finn-b-M13_SLE (https://gwas.mrcieu.ac.uk/datasets/finn-b-M13_SLE/)."], "basis": "The paper names GSE65391 in GEO data repository and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["mr_estimate"], "assets": ["source:pmid:39445158", "data:geo:GSE65391"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE65391"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T19:36:37Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 8892, "completion_tokens": 2479, "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:39445158", "type": "paper", "label": "pmid:39445158"}, {"id": "source:pmid:39445158", "type": "manuscript", "label": "PMID 39445158 full text"}, {"id": "data:geo:GSE65391", "type": "dataset", "label": "GEO GSE65391"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:39445158", "type": "paper", "label": "pmid:39445158"}, {"id": "source:pmid:39445158", "type": "manuscript", "label": "PMID 39445158 full text"}, {"id": "data:geo:GSE65391", "type": "dataset", "label": "GEO GSE65391"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-39445158", "type": "extraction", "used": ["source:pmid:39445158"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE65391", "type": "accession_resolution", "used": ["data:geo:GSE65391"], "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:39445158", "parent_hash": "sha256:ec47da339ecc5e9a432d6706897309f07d60be769f7aa4a47e788ecd9dc41780", "fork_of": null, "generation": 1}, "gaps": ["asset:data:geo:GSE65391 licence not read", "asset:data:geo:GSE65391 not downloaded", "execution.not_run", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "analysis", "definition_id": "genetic_association", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["mr_estimate"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["We employed a two-sample Mendelian randomization (MR) analysis to investigate the causal relationship between characteristic genes and the risk of systemic lupus erythematosus (SLE), using single nucleotide polymorphisms (SNPs) as instrumental variables."], "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":"2026-11-04T03:13:23+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}}}