{"data":{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39702336", "kind": "science-record", "revision": 4, "content_hash": "sha256:e45b3dd541c288539cabf6c99865ba796fafb5cd16a875e4f177830224e8c336", "created_at": "2026-11-01T20:30:01Z", "updated_at": "2026-11-01T22:55:52Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "The role of m6A methylation genes in predicting poor prognosis in sepsis: identifying key biomarkers and therapeutic targets.", "doi": "10.1186/s40001-024-02194-8", "pmid": "39702336", "authors": ["Shaokang Wang", "Siye Shen", "Na Cheng", "Wenjun Zhou", "Weili Yu", "Daiyun Liang", "Lijun Cao", "Pinjie Zhang", "Zhonghua Lu", "Yun Sun"], "journal": "European journal of medical research", "year": 2024, "abstract": "Sepsis is one of the leading causes of death among seriously ill patients worldwide, affecting more than 30 million people annually and accounting for 1-2% of hospitalizations. By analyzing gene expression omnibus (GEO) data set, our team explored the relationship between m6A methylation gene and poor prognosis of sepsis. The purpose of this present study is to examine new detection markers for patients with poor prognosis, provide theoretical basis for timely intervention and improve the survival rate of patients. First, GSE54514 transcriptome data were extracted from the GEO database 31 patients with sepsis related death and 72 sepsis survivors. Key genes were screened from differentially expressed genes (DEGs), least absolute shrinkage and selection operator (LSAAO) and random forest (RF). And then, METTL3, WTAP and RBM15 were further verified by quantitative reverse transcription PCR (qRT-PCR). The constructed nomogram model showed high accuracy in predicting death. These three genes are mainly involved in chemokine signaling pathway, differentiation of monocytes and T cells, and phagocytosis of immune cells. The analysis showed that a high m6A score subtype is linked to lower immunosuppression and higher survival rates in clinical samples, suggesting better immune responses and outcomes for these patients. Finally, the protective effect of METTL3 in sepsis was demonstrated in mouse sepsis model applied with METTL3 inhibitor, by conducting cell flow cytometry analysis, enzyme-linked immunosorbent assay (ELISA) and hematoxylin-eosin (HE) staining. In conclusion, these findings provide potential biomarkers and targets for early precision diagnosis and treatment.", "claims": [{"id": "claim-method", "statement": "Three feature selection algorithms—SVM–RFE [17], LASSO logistic regression [18], and RF algorithm [19]—were used to screen sepsis survival-related biomarkers.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39702336", "locator": "sections[6] \"Identification of hub genes in sepsis\" paragraph 2", "quote": "Three feature selection algorithms—SVM–RFE [17], LASSO logistic regression [18], and RF algorithm [19]—were used to screen sepsis survival-related biomarkers."}], "state": "complete"}, {"id": "claim-data-access", "statement": "No datasets were generated or analysed during the current study.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39702336", "locator": "sections[30] \"notes\" paragraph 1", "quote": "No datasets were generated or analysed during the current study."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The constructed nomogram model showed high accuracy in predicting death.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39702336", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "The constructed nomogram model showed high accuracy in predicting death."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The constructed nomogram model showed high accuracy in predicting death.", "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:39702336"], "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:GSE54514"], "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:GSE54514"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39702336", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s40001-024-02194-8", "sha256": 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The constructed nomogram model sh"], "order_basis": "paper_text_order:943", "id": "assay:1"}, {"role": "data_generation", "definition_id": "animal_model", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["fect of METTL3 in sepsis was demonstrated in mouse sepsis model applied with METTL3 inhibitor,", "regulatory factors, for validation. Fifteen mice were divided into three groups: normal control gr", "ed to evaluate pro-inflammatory cytokines in mouse lung tissue. Treatment of groups: The CLP +", "antibodies used included: PE-conjugated anti-mouse IL-17A, BV421-conjugated anti-mouse CD4, Per"], "order_basis": "paper_text_order:1485", "id": "assay:2"}, {"role": "data_generation", "definition_id": "flow_cytometry", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["ed with METTL3 inhibitor, by conducting cell flow cytometry analysis, enzyme-linked immunosorbent assay", "TM2457 + sepsis model group (CLP + STM2457). Flow cytometry, enzyme-linked immunosorbent assay (ELISA),", "stablishment, tissue samples were collected. Flow cytometry: Cells were isolated from spleen tissue by g"], "order_basis": "paper_text_order:1554", "id": "assay:3"}, {"role": "data_generation", "definition_id": "elisa", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["analysis, enzyme-linked immunosorbent assay (ELISA) and hematoxylin-eosin (HE) staining. In con", "by conducting cell flow cytometry analysis, enzyme-linked immunosorbent assay (ELISA) and hematoxylin-eosin (HE) staining.", "ytometry, enzyme-linked immunosorbent assay (ELISA), and HE staining were used to evaluate pro-", "to assess specific immune cell infiltration. ELISA: Blood was collected from the orbital area a"], "order_basis": "paper_text_order:1580", "id": "assay:4"}, {"role": "analysis", "definition_id": "principal_component_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["pca_fit"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Three feature selection algorithms—SVM–RFE [17], LASSO logistic regression [18], and RF algorithm [19]—were used to screen sepsis survival-related biomarkers."], "order_basis": "consumes_earlier_output", "id": "assay:5"}]},"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. 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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}}}