Prognostic risk modeling of endometrial cancer using programmed cell death-related genes: a comprehensive machine learning approach.

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combination demonstrated excellent predictive accuracy (AUC > 0.8).", "target_result": "The model achieved an AUC > 0.8.", "number_stated": true, "source_locator": "chars 931-1039 of source sha256 1d76360e1eff4904d83d9f3dca5eb18158ebd2976d68d00bdc5d3ae14507559a"}, "recorded_at": "2026-10-31T15:33:23.956847+00:00"}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "replication-target-v2", "prompt_hash": "42ea7853e7b1e1417f8bb73896227f76cfead73fe4c41912ac5f58fc4a10eb56", "source_hash": "1d76360e1eff4904d83d9f3dca5eb18158ebd2976d68d00bdc5d3ae14507559a", "raw_response": "<think>We need answer JSON only. Need identify single numerical result headline main finding registered target. Paper abstract: StepCox+plsRcox excellent predictive accuracy (AUC > 0.8). Likely headline number 0.8, unit AUC? User asks value paper itself states, unit paper prints. Need parse. \"single numerical result that a replication ... reproduce. Report headline number only: value paper itself states for registered target\". Main finding likely AUC > 0.8. Could be HR 3.37 etc. Headline numerical result probably AUC > 0.8, numerical headline. Need parent_value number 0.8, unit maybe \"AUC\" (unitless? Paper prints AUC > 0.8). Evidence exact contiguous quote containing number and target attribution: \"The StepCox[forward] + plsRcox algorithm combination demonstrated excellent predictive accuracy (AUC > 0.8).\" target_result perhaps \"AUC > 0.8\". number_stated true. But \"headline number only\" likely value 0.8. Unit must paper prints. Could unit be \"AUC\" perhaps. Is AUC a unit? Printed metric. JSON parent_value numeric cannot encode >; target_result can say \"AUC > 0.8\". They likely expect unit \"AUC\" or perhaps empty? Instruction \"unit must be unit paper prints.\" The paper prints AUC, not a unit. Usually extraction task asks value and unit. Could set unit \"AUC\" perhaps accepted. Need perhaps \"AUC\" as metric label. Evidence exact quote. Let's ensure no issue that > 0.8 is threshold rather than exact result. Yet source states numerical result. The headline result likely model AUC > 0.8. Other headline 10 critical genes and 117 algorithms, but \"main finding\" perhaps prognostic model with AUC >0.8. User wants single numerical result. Yes.\n\nCould HR 3.37 be more headline? Abstract emphasizes predictive accuracy AUC > .8 and survival differences HR. \"headline numerical result the paper states for its main finding.\" Target generic. Usually benchmark target probably asks extract one numerical result associated with target. Main finding is model performance. AUC >0.8 headline. The result sentence immediately after model construction. Number 0.8.\n\nNeed exact contiguous quote, whole clause. \"The StepCox[forward] + plsRcox algorithm combination demonstrated excellent predictive accuracy (AUC > 0.8).\" This attaches target. Unit perhaps \"AUC\". target_result perhaps \"AUC > 0.8\".</think>\n\n{\"parent_value\":0.8,\"unit\":\"AUC\",\"evidence\":\"The StepCox[forward] + plsRcox algorithm combination demonstrated excellent predictive accuracy (AUC > 0.8).\",\"target_result\":\"AUC > 0.8\",\"number_stated\":true}", "reading": {"parent_value": 0.8, "unit": "AUC", "evidence": "The StepCox[forward] + plsRcox algorithm combination demonstrated excellent predictive accuracy (AUC > 0.8).", "target_result": "AUC > 0.8", "number_stated": true, "source_locator": "chars 931-1039 of source sha256 1d76360e1eff4904d83d9f3dca5eb18158ebd2976d68d00bdc5d3ae14507559a"}, "recorded_at": "2026-10-31T15:33:28.162616+00:00"}], "agreement": false, "disagreements": {"target_result": {"minimax": "The StepCox[forward] + plsRcox prognostic model achieved excellent predictive accuracy, with AUC > 0.8.", "minimax_alt": "Kaplan–Meier analysis revealed significant survival differences between high- and low-risk groups in both training (HR = 3.37, p < 0.001) and validation cohorts (HR = 2.05, p = 0.021).", "decisive": false}, "analysis_methods": {"minimax": ["117 machine learning algorithms", "StepCox[forward] + plsRcox", "Kaplan–Meier analysis", "differential gene expression analysis", "weighted gene co-expression network analysis", "functional enrichment analysis", "immune landscape evaluation", "multi-dimensional risk stratification"], "minimax_alt": ["117 machine learning algorithms", "StepCox[forward] + plsRcox algorithm combination", "Kaplan–Meier analysis", "differential gene expression analysis using the limma package in R", "weighted gene co-expression network analysis (WGCNA)", "STRING protein–protein interaction network analysis", "GO and KEGG pathway enrichment analyses using clusterProfiler", "gene set enrichment analysis (GSEA)", "immune cell infiltration analysis", "somatic mutation analysis", "drug sensitivity analysis"], "decisive": false}, "statistical_method": {"minimax": "StepCox[forward] + plsRcox algorithm combination", "minimax_alt": "Kaplan–Meier analysis", "decisive": true}}, "agreement_basis": {"policy": "decisive_fields", "decisive_fields": ["work_type", "number_stated", "parent_value", "unit"]}, "blinded": true}, "human_critique": {"status": "not_run", "reviewer": null, "signed_at": null, "conflict_checked": false, "commentary": null}, "stages": [{"key": "target_materials", "status": "pending", "reason": null, "evidence": null}, {"key": "independent_readings", "status": "blocked", "reason": "The independent readings are incomplete or disagree; the source evidence must resolve the difference.", "evidence": {"provider_errors": []}}, {"key": "source_resolution", "status": "pending", "reason": null, "evidence": null}, {"key": "protocol_reconstruction", "status": "pending", "reason": null, "evidence": null}, {"key": "control_execution", "status": "pending", "reason": null, "evidence": null}, {"key": "control_comparison", "status": "pending", "reason": null, "evidence": null}, {"key": "publication", "status": "pending", "reason": null, "evidence": null}], "gaps": ["review.human_critique", "review.machine_critique.blocked"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "package:8106bbd890e1494d87989d8c342b1cfa83f67b2920fcf46354b1e447423da129", "type": "sep_package", "label": "SEP v1 package for pmid:40056247"}], "activities": [{"id": "activity:conversion:pmid:40056247", "type": "conversion", "used": ["package:8106bbd890e1494d87989d8c342b1cfa83f67b2920fcf46354b1e447423da129"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:minimax", "type": "model", "label": "minimax"}, {"id": "agent:minimax_alt", "type": "model", "label": "minimax_alt"}, {"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}], "source_run_id": "run-20261023T122449232913", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "3c946e88-b6ca-46a9-9fe3-3b4ac0ac502a", "parent_hash": "8106bbd890e1494d87989d8c342b1cfa83f67b2920fcf46354b1e447423da129", "fork_of": null, "generation": 1}, "gaps": ["abstract", "asset:source:pmid:40056247 not verified", "authors", "capability", "corpus_record_absent", "execution.not_run", "method", "review.human_critique", "review.machine_critique.blocked", "step:obtain_source_data blocked on data:not_downloaded", "step:run_analysis blocked on no owned computational capability is bound to this experiment", "step:run_analysis input 'materials:the source data behind the published result in pmid:40056247' does not resolve to a declared asset", "targets"]}