{"data":{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36313466", "kind": "science-record", "revision": 4, "content_hash": "sha256:761fa3a6dba606f7c004b41ee78fbf63533a6489bf2d1695482fb01019c77a2f", "created_at": "2026-11-01T18:15:44Z", "updated_at": "2026-11-01T22:07:59Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Definition of immune molecular subtypes with distinct immune microenvironment, recurrence, and PANoptosis features to aid clinical therapeutic decision-making.", "doi": "10.3389/fgene.2022.1007108", "pmid": "36313466", "authors": ["Sufeng Qiang", "Fei Fu", "Jianjun Wang", "Chunyan Dong"], "journal": "Frontiers in genetics", "year": 2022, "abstract": "Objective: Cervical cancer poses a remarkable health burden to females globally. Despite major advances in early detection and treatment modalities, some patients still relapse. The present study proposed a novel immune molecular classification that reflected distinct recurrent risk and therapeutic responses in cervical cancer. Methods: We retrospectively collected two cervical cancer cohorts: TCGA and GSE44001. Consensus clustering approach was conducted based on expression profiling of recurrence- and immune-related genes. The abundance of immune cells was inferred via five algorithms. Immune functions and signatures were quantified through ssGSEA. Genetic mutations were analyzed by maftools package. Immunotherapeutic response was inferred via tumor mutation burden (TMB), Tumor Immune Dysfunction and Exclusion (TIDE), and Submap methods. Finally, we developed a LASSO model for recurrence prediction. Results: Cervical cancer samples were categorized into two immune subtypes (IC1, and IC2). IC2 exhibited better disease free survival (DFS), increased immune cell infiltration within the immune microenvironment, higher expression of immune checkpoints, higher activity of immune-relevant pathways (APC co-inhibition and co-stimulation, inflammation-promoting, MHC class I, IFN response, leukocyte and stromal fractions, macrophage regulation, and TCR Shannon), and higher frequencies of genetic mutations. This molecular classification exhibited a remarkable difference with existing immune subtypes, with diverse PANoptosis (pyroptosis, apoptosis and necroptosis) features. Patients in IC2 were more likely to respond to immunotherapy and targeted, and chemotherapeutic agents. The immune subtype-relevant signature was quantified to predict patients' recurrence risk. Conclusion: Altogether, we developed an immune molecular classification, which can be utilized in clinical practice to aid decision-making on recurrence management.", "claims": [{"id": "claim-method", "statement": "Univariate cox regression analysis of IRGs was implemented based on recurrence time and status both in the TCGA and GSE44001 datasets. In accordance with p < 0.05, recurrence-related IRGs were obtained, with hazard ratio (HR)>1 as a risk factor and HR < 1 as a protective factor. Then, protective and risk factors of the two datasets were separately intersected for subsequent consensus clustering analysis. Unsupervised consensus clustering was implemented for constructing an immune molecular classification using ConsensusClusterPlus package (version 1.52.0) based on recurrence-related IRGs (Wilkerson and Hayes, 2010).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36313466", "locator": "sections[4] \"Unsupervised consensus clustering analysis\" paragraph 2", "quote": "Univariate cox regression analysis of IRGs was implemented based on recurrence time and status both in the TCGA and GSE44001 datasets. In accordance with p < 0.05, recurrence-related IRGs were obtained, with hazard ratio (HR)>1 as a risk factor and HR < 1 as a protective factor. Then, protective and risk factors of the two datasets were separately intersected for subsequent consensus clustering analysis. Unsupervised consensus clustering was implemented for constructing an immune molecular classification using ConsensusClusterPlus package (version 1.52.0) based on recurrence-related IRGs (Wilkerson and Hayes, 2010)."}], "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:36313466", "locator": "sections[28] \"Data availability statement\" paragraph 2", "quote": "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."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Cervical cancer samples were categorized into two immune subtypes (IC1, and IC2).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36313466", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Cervical cancer samples were categorized into two immune subtypes (IC1, and IC2)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Cervical cancer samples were categorized into two immune subtypes (IC1, and IC2).", "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:36313466"], "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:GSE44001"], "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:GSE44001"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36313466", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fgene.2022.1007108", "sha256": "sha256:eedbac81e16e350ef8d1222be1c6251e67e4634d81842d59f4fa2d10f4613cc4", "bytes": 39177, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fgene.2022.1007108", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9606342", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE44001", "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=GSE44001", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE44001", "version": null, "note": "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.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets presented in this study can be found in online repositories. 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Patients in IC2 w", "E S3The gene sets of PANoptosis (pyroptosis, apoptosis and necroptosis). SUPPLEMENTARY TABLE S4The"], "order_basis": "paper_text_order:1668", "id": "assay:1"}, {"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 regression analysis of IRGs was implemented based on recurrence time and status both in the TCGA and GSE44001 datasets. In accordance with p < 0.05, recurrence-related IRGs were obtained, with hazard ratio (HR)>1 as a risk factor and HR < 1 as a protective factor. Then, protective and risk factors of the two datasets were separately intersected for subsequent consensus clustering analysis. Unsupervised consensus clustering was implemented for constructing an immune molecular classification using ConsensusClusterPlus package (version 1.52.0) based on recurrence-related IRGs (Wilkerson and Hayes, 2010)."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]},"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. 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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}}}