Identification of the key immune gene NR3C1 as a diagnostic biomarker in differentiating ovarian borderline tumors from benign tumors.

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null, "source": "chars 1764-2036 of source sha256 efc80e530b1347985bddb570d2b3b6796358adae65e222766672cd38164d0922"}, "evidence": [{"asset": "source:pmid:40959664", "locator": "chars 1764-2036 of source sha256 efc80e530b1347985bddb570d2b3b6796358adae65e222766672cd38164d0922", "quote": "FGFR3, GNAI1, NR3C1, and PDGFA were found to have potential diagnostic value for BOTs (AUCFGFR3 = 0.883, AUCGNAI1 = 0.789, AUCNR3C1 = 0.760, AUCPDGFA = 0.783) and further validated in the test group (AUCFGFR3 = 0.917, AUCGNAI1 = 0.900, AUCNR3C1 = 0.867, AUCPDGFA = 0.833)."}]}], "gaps": ["abstract", "authors"]}, "experiment": {"state": "partial", "objective": "the headline numerical result the paper states for its main finding", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate and its interval from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The number a replication must land on, and the sentence it came from.", 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λ: lambda.min- nfolds: 10-Type.measure: Devian", "FGFR3, PDGFA, GNAI1, AR, NR3C1, FGFR1, IGF1, HRAS"], ["SVM-REF", "- Kernel: Radial- Step size (k): 10- halve. above: 50- nfold: 10", "PDGFA, HRAS, GNAI1, NR3C1, FGFR3, IGF1, AR, FGFR1"]], "row_count": 3, "column_count": 3, "numeric_cells": 2, "header_rows": 1, "locator": "section[19]/table-wrap[1]#T2"}}], "gaps": ["asset:source:pmid:40959664 not verified"]}, "analysis": {"state": "absent", "methods": [], "targets": [], "gaps": ["method", "targets"]}, "capability": {"state": "absent", "requirements": [], "gaps": ["capability"]}, "readiness": {"state": "partial", "tier": null, "blocking_axis": null, "blocked_on": null, "axes": {}, "can_run": {"dsl_operations": [], "assets": ["source:pmid:40959664", "asset:table:pmid:40959664:1", "asset:table:pmid:40959664:2"], "steps": ["read_published_estimate"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": ["xml"], "capabilities": [], "data_gaps": ["source:pmid:40959664"]}, "gaps": ["corpus_record_absent"]}, "review": {"state": "partial", "machine_critique": {"status": "blocked", "providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "full-text-reading-v1", "prompt_hash": "c987407c5c8d5f93c30e962481dd40888e17d2a5192828e211980bde5afb669b", "source_hash": "efc80e530b1347985bddb570d2b3b6796358adae65e222766672cd38164d0922", "raw_response_sha256": "sha256:186fef0038675754930ac6d9bc2d1070777da829867b31cae5995745f0de5b9d", "raw_response_chars": 5134}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "full-text-reading-v1", "prompt_hash": "994ef2e95ccd5fd7aa0359718e141d8cbbba8efb43855414e4f7a7d98399cbc8", "source_hash": "efc80e530b1347985bddb570d2b3b6796358adae65e222766672cd38164d0922", "raw_response_sha256": "sha256:42438600715db6e7f0e224e2a15c5e7077e6c2a4be6932b39780ca39c9fa7aee", "raw_response_chars": 29018}], "target_providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "replication-target-v2", "prompt_hash": "4e02dbfa385f30895cbc7a7d2351fd40c9af0aaa5adaa139bb7180311f377e67", "source_hash": "efc80e530b1347985bddb570d2b3b6796358adae65e222766672cd38164d0922", "raw_response": "{\"parent_value\":0.867,\"unit\":\"AUC\",\"evidence\":\"FGFR3, GNAI1, NR3C1, and PDGFA were found to have potential diagnostic value for BOTs (AUCFGFR3 = 0.883, AUCGNAI1 = 0.789, AUCNR3C1 = 0.760, AUCPDGFA = 0.783) and further validated in the test group (AUCFGFR3 = 0.917, AUCGNAI1 = 0.900, AUCNR3C1 = 0.867, AUCPDGFA = 0.833).\",\"target_result\":\"NR3C1 diagnostic AUC in the independent test group\",\"number_stated\":true}", "reading": {"parent_value": 0.867, "unit": "AUC", "evidence": "FGFR3, GNAI1, NR3C1, and PDGFA were found to have potential diagnostic value for BOTs (AUCFGFR3 = 0.883, AUCGNAI1 = 0.789, AUCNR3C1 = 0.760, AUCPDGFA = 0.783) and further validated in the test group (AUCFGFR3 = 0.917, AUCGNAI1 = 0.900, AUCNR3C1 = 0.867, AUCPDGFA = 0.833).", "target_result": "NR3C1 diagnostic AUC in the independent test group", "number_stated": true, "source_locator": "chars 1764-2036 of source sha256 efc80e530b1347985bddb570d2b3b6796358adae65e222766672cd38164d0922"}, "recorded_at": "2026-10-31T15:35:54.176825+00:00"}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "replication-target-v2", "prompt_hash": "8d6c8e63bcea884d216740d0d2af1be781ff1a5ad8bd37b871350b4e572f2872", "source_hash": "efc80e530b1347985bddb570d2b3b6796358adae65e222766672cd38164d0922", "raw_response": "<think>We need produce JSON object with headline numerical result paper states main finding registered target. Need exact contiguous quote containing number, whole clause attribution, paper printed unit. Need consider which number: main finding NR3C1 diagnostic biomarker, headline numerical result likely AUC NR3C1 = 0.760 discovery and further validated test group AUC NR3C1 = 0.867. User asks single numerical result replication of registered target has to reproduce \"headline number only: value the paper itself states for the registered target, not a value you calculated, converted, averaged or reconstructed.\" Registered target: headline numerical result paper states for its main finding. Main finding NR3C1. Source title identification key immune gene NR3C1 diagnostic biomarker differentiating ovarian borderline tumors from benign tumors. Abstract: \"FGFR3, GNAI1, NR3C1, and PDGFA were found to have potential diagnostic value for BOTs (AUCFGFR3 = 0.883, AUCGNAI1 = 0.789, AUCNR3C1 = 0.760, AUCPDGFA = 0.783) and further validated in the test group (AUCFGFR3 = 0.917, AUCGNAI1 = 0.900, AUCNR3C1 = 0.867, AUCPDGFA = 0.833).\" Headline number might be NR3C1 validation AUC 0.867 as main finding. Need decide. It likely is 0.867 (test group validated diagnostic performance). Evidence exact quote whole clause with list. Need unit AUC dimensionless \"AUC\" paper prints no unit. Could unit \"AUC\" appropriate. Could \"unit\": \"AUC\" although AUC is unitless metric and printed notation. They explicitly say unit paper prints. Paper prints AUCNR3C1 = 0.867 (no space). We can evidence full clause \"FGFR3, GNAI1, NR3C1, and PDGFA were found to have potential diagnostic value for BOTs (AUCFGFR3 = 0.883, AUCGNAI1 = 0.789, AUCNR3C1 = 0.760, AUCPDGFA = 0.783) and further validated in the test group (AUCFGFR3 = 0.917, AUCGNAI1 = 0.900, AUCNR3C1 = 0.867, AUCPDGFA = 0.833).\" Number .867. target_result maybe \"NR3C1 had an AUC of 0.867 in the independent test group.\" Unit \"AUC\" or \"\"? User says unit must be unit paper prints. Since paper prints AUC no physical unit. Could use \"AUC\" as unit. Need assess desired patterns of task perhaps from benchmark. They expect parent_value number, unit maybe \"AUC\", evidence quote, target_result phrase. The target headline main finding \"NR3C1 ... diagnostic biomarker\" number likely AUC .867 (validation) because headline is validation. Need think carefully.\n\nMaybe headline numerical result could be \"26 DIRGs\" instead? Registered target = headline numerical result for main finding. Main finding \"NR3C1\" gene, so its AUC. They ask single numerical result replication of registered target has to reproduce. Many source snippets include multiple AUCs. Need map NR3C1 correctly. \"headline number only\" probably intended answer 0.867 (validated). Source abstract gives NR3C1 AUC .760 and .867; \"headline\" usually result stated main summary including validated test group. Which one paper itself states for target? Both, so choose more headline validated. Need avoid calculation. We can quote clause encompassing both. No ambiguity communicated? Can note in target_result as \"NR3C1's diagnostic AUC in the independent test group was 0.867.\" The evidence whole sentence. Unit \"AUC\". Fine.\n\nMaybe evidence shorter contiguous quotation \"(AUCFGFR3 = 0.917, AUCGNAI1 = 0.900, AUCNR3C1 = 0.867, AUCPDGFA = 0.833)\" allowed because parenthetical list attaches target by list and word respectively order; user says \"Attribution may be carried by text's own wording: a number may be attached to the target by a parenthetical list, by the word 'respectively', or by words that name the target directly. When that is how the text links them, quote the whole clause so a reader can check the attribution for themselves.\" Better quote whole sentence to be safe, as \"whole clause\" might encompass full sentence. Use exact full sentence.\n\nUnit maybe \"AUC\" should be lowercase? Printed AUC. Parent .867. target_result concise.\n\nLet's deeply consider if expected .760 instead. The \"headline numerical result\" abstract lists first AUC in parentheses \"found to have potential diagnostic value\" then \"further validated in test group\". Title NR3C1. Main headline AUC could be .867 test group, best. Typical scientific abstract headlines validated performance final. We should pick .867. They may score numeric exact. Shouldn't output multiple. We have one.\n\nWe can respond JSON only, no fences. Evidence sentence no ellipsis. Unit \"AUC\" (dimensionless). target_result \"NR3C1 achieved an AUC of 0.867 in the independent test group for distinguishing BOTs from BeOTs.\" This adds distinguishing etc from title context no issue. \"achieved\" paraphrase target result okay. Need not evidence exact in target_result. number_stated true.\n\nWe need not mention P<.05 etc. All good.</think>\n\n{\"parent_value\":0.867,\"unit\":\"AUC\",\"evidence\":\"FGFR3, GNAI1, NR3C1, and PDGFA were found to have potential diagnostic value for BOTs (AUCFGFR3 = 0.883, AUCGNAI1 = 0.789, AUCNR3C1 = 0.760, AUCPDGFA = 0.783) and further validated in the test group (AUCFGFR3 = 0.917, AUCGNAI1 = 0.900, AUCNR3C1 = 0.867, AUCPDGFA = 0.833).\",\"target_result\":\"NR3C1 had a diagnostic AUC of 0.867 in the independent test group.\",\"number_stated\":true}", "reading": {"parent_value": 0.867, "unit": "AUC", "evidence": "FGFR3, GNAI1, NR3C1, and PDGFA were found to have potential diagnostic value for BOTs (AUCFGFR3 = 0.883, AUCGNAI1 = 0.789, AUCNR3C1 = 0.760, AUCPDGFA = 0.783) and further validated in the test group (AUCFGFR3 = 0.917, AUCGNAI1 = 0.900, AUCNR3C1 = 0.867, AUCPDGFA = 0.833).", "target_result": "NR3C1 had a diagnostic AUC of 0.867 in the independent test group.", "number_stated": true, "source_locator": "chars 1764-2036 of source sha256 efc80e530b1347985bddb570d2b3b6796358adae65e222766672cd38164d0922"}, "recorded_at": "2026-10-31T15:36:12.373247+00:00"}], "agreement": false, "disagreements": {"target_result": {"minimax": "NR3C1 distinguished borderline ovarian tumors from benign ovarian tumors with an AUC of 0.760 in the discovery analysis and an AUC of 0.867 in the independent test group.", "minimax_alt": "NR3C1 diagnostic AUC = 0.760 in the reported biomarker analysis and AUC = 0.867 in the independent test group.", "decisive": false}, "physical_methods": {"minimax": ["Immunohistochemistry (IHC) analysis", "Evaluation of an independent test group"], "minimax_alt": ["immunohistochemistry (IHC) analysis"], "decisive": false}, "analysis_methods": {"minimax": ["Gene Ontology (GO) enrichment analysis", "Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis", "Protein-protein interaction (PPI) network analysis", "Least absolute shrinkage and selection operator (LASSO) regression", "Support vector machine-recursive feature elimination (SVM-RFE)", "Receiver operating characteristic (ROC) analysis", "Mann-Whitney U test", "Chi-square test", "Spearman’s correlation", "Unpaired t-test", "CIBERSORT algorithm"], "minimax_alt": ["microarray expression-profile integration", "Gene Ontology (GO) enrichment analysis", "Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis", "protein-protein interaction (PPI) network analysis", "Least absolute shrinkage and selection operator (LASSO) regression", "support vector machine-recursive feature elimination (SVM-RFE)", "Receiver operating characteristic (ROC) analysis", "nomogram construction", "CIBERSORT algorithm", "Mann-Whitney U test", "Chi-square test", "Spearman’s correlation", "unpaired t-test"], "decisive": false}, "statistical_method": {"minimax": "Receiver operating characteristic (ROC) analysis", "minimax_alt": "Receiver operating characteristic (ROC) curve 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; 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