SLC3A2 as a key anoikis-related gene for prognosis and tumor microenvironment remodeling in melanoma.

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"sha256:b7d140bc7384a8cca3fb686d93a2542cb3b2b20cd02e87ba0ec6b2079d1b60dd", "raw_response_chars": 5158}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "full-text-reading-v1", "prompt_hash": "bbdd27a1e628c1c106f7ed2526f204dffb9f96407c360b7ff115f313780997d0", "source_hash": "e9a29ea853dfa9b33a4ec4461eace4ec0ca68f743f12c323117270c38f79e4ff", "raw_response_sha256": "sha256:f099170b55bcdd6b3b9125dedbf4038673e49b9ed1154a8bd56339b84a5cbbd1", "raw_response_chars": 50997}], "target_providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "replication-target-v2", "prompt_hash": "30ec5282917b8a40a2763b34864026ff42896db3eac60023ad008b7fbd710026", "source_hash": "e9a29ea853dfa9b33a4ec4461eace4ec0ca68f743f12c323117270c38f79e4ff", "raw_response": "{\"parent_value\":0.774,\"unit\":\"C-index\",\"evidence\":\"The “random survival forest + gradient boosting machine” model demonstrated optimal prognostic performance across the TCGA training cohort and validation cohorts (GSE19234, GSE22153, GSE65904), with a C-index of 0.774.\",\"target_result\":\"The optimal prognostic model achieved a C-index of 0.774.\",\"number_stated\":true}", "reading": {"parent_value": 0.774, "unit": "C-index", "evidence": "The “random survival forest + gradient boosting machine” model demonstrated optimal prognostic performance across the TCGA training cohort and validation cohorts (GSE19234, GSE22153, GSE65904), with a C-index of 0.774.", "target_result": "The optimal prognostic model achieved a C-index of 0.774.", "number_stated": true, "source_locator": "chars 1801-2019 of source sha256 e9a29ea853dfa9b33a4ec4461eace4ec0ca68f743f12c323117270c38f79e4ff"}, "recorded_at": "2026-10-31T15:26:26.212447+00:00"}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "replication-target-v2", "prompt_hash": "5130774df1eae56950ad7ba5a8342bdae042b6cfefb4e6c9b29037bac98a67e0", "source_hash": "e9a29ea853dfa9b33a4ec4461eace4ec0ca68f743f12c323117270c38f79e4ff", "raw_response": "<think>We need answer JSON only. Need identify single numerical result replication registered target headline numerical result paper states main finding. Likely C-index 0.774 for optimal prognostic model. Need unit paper prints. Unit perhaps \"C-index\" or no unit? Schema parent_value number, unit string. Evidence exact contiguous quote contains number, whole clause attribution. Candidate: \"The “random survival forest + gradient boosting machine” model demonstrated optimal prognostic performance across the TCGA training cohort and validation cohorts (GSE19234, GSE22153, GSE65904), with a C-index of 0.774.\" Number 0.774. Unit paper prints: C-index perhaps unit is \"C-index\". Headline number only. target_result concise. Need JSON valid, exact quote. Could unit be \"C-index\" although that's metric not unit. They insist unit must be unit paper prints. Paper prints \"a C-index of 0.774\", so likely unit \"C-index\". Number stated true.\n\nBut target says headline numerical result paper states for main finding. Main finding could title SLC3A2, but headline numerical result model C-index 0.774. Other number AUC range. Single headline likely C-index. Need exact contiguous quote. We can quote full sentence exactly as source. Ensure curly quotation marks. JSON escaping not issue. \"parent_value\":0.774, \"unit\":\"C-index\", evidence exact. target_result perhaps \"The random survival forest + gradient boosting machine model achieved a C-index of 0.774 across the TCGA training cohort and validation cohorts.\" That's okay but perhaps \"headline number only\" means report number only maybe target_result should be \"0.774\" or describe? Required schema asks target_result. User says report headline number only, value paper itself states. We can target_result maybe \"C-index of 0.774\". But output includes evidence necessarily. Unit \"C-index\". Let's ensure exact quote punctuation and Unicode. Sentence appears twice; use one exact. Full clause enough, exact contiguous quotation. Could quote \"with a C-index of 0.774.\" but attribution through preceding wording? User says quote whole clause so check attribution. Full sentence best.\n\nNeed no markdown.</think>\n\n{\"parent_value\":0.774,\"unit\":\"C-index\",\"evidence\":\"The “random survival forest + gradient boosting machine” model demonstrated optimal prognostic performance across the TCGA training cohort and validation cohorts (GSE19234, GSE22153, GSE65904), with a C-index of 0.774.\",\"target_result\":\"C-index of 0.774\",\"number_stated\":true}", "reading": {"parent_value": 0.774, "unit": "C-index", "evidence": "The “random survival forest + gradient boosting machine” model demonstrated optimal prognostic performance across the TCGA training cohort and validation cohorts (GSE19234, GSE22153, GSE65904), with a C-index of 0.774.", "target_result": "C-index of 0.774", "number_stated": true, "source_locator": "chars 1801-2019 of source sha256 e9a29ea853dfa9b33a4ec4461eace4ec0ca68f743f12c323117270c38f79e4ff"}, "recorded_at": "2026-10-31T15:26:27.634885+00:00"}], "agreement": true, "disagreements": {"target_result": {"minimax": "The random survival forest + gradient boosting machine prognostic model achieved a C-index of 0.774.", "minimax_alt": "The “random survival forest + gradient boosting machine” model demonstrated optimal prognostic performance across the TCGA training cohort and validation cohorts (GSE19234, GSE22153, GSE65904), with a C-index of 0.774.", "decisive": false}, "physical_methods": {"minimax": ["shRNA-mediated knockdown", "colony formation assays", "Transwell migration assays"], "minimax_alt": ["shRNA-mediated knockdown of SLC3A2", "colony formation assay", "Transwell migration assay"], "decisive": false}, "analysis_methods": {"minimax": ["random survival forest", "gradient boosting machine", "concordance index (C-index)", "Kaplan–Meier survival analysis", "time-dependent ROC curves", "Wilcoxon rank-sum test", "Pearson correlation analysis", "Gene Set Variation Analysis (GSVA)", "gene set enrichment analysis (GSEA)", "log-rank test"], "minimax_alt": ["Harmony algorithm for batch-effect correction", "t-SNE dimensionality reduction", "AUCell enrichment scoring", "CellChat intercellular communication analysis", "Pearson correlation analysis", "Cox regression", "random survival forest", "gradient boosting machine", "concordance index (C-index)", "Kaplan–Meier survival analysis", "time-dependent ROC curves", "ESTIMATE", "CIBERSORT", "TIMER", "xCell", "EPIC", "MCPcounter", "QUANTISEQ", "ssGSEA", "Gene Set Variation Analysis (GSVA)", "Gene Set Enrichment Analysis (GSEA)", "Wilcoxon test", "Wilcoxon rank-sum test", "log-rank test"], "decisive": false}}, "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": "passed", "reason": "The independent readings are incomplete or disagree; 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