Construction of a prognostic risk model for acute myeloid leukemia based on exosomal genes and analysis of immune microenvironment characteristics.

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"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:40890357", "asset:table:pmid:40890357:1", "asset:table:pmid:40890357:2", "asset:table:pmid:40890357:3", "asset:table:pmid:40890357:4"], "steps": ["read_published_estimate"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": ["xml"], "capabilities": [], "data_gaps": ["source:pmid:40890357"]}, "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": "58ee5d6ef5c259f3bc4cad5a02cdd9176e080ab5ef2ece6a36170d230f8518c6", "source_hash": "6f9810fe91bf2a45614e234d57513e9050e672dc8eec96a2e1fd1a89e225877e", "raw_response_sha256": "sha256:53c655a7316dc70e16f4271e9f7512554b36d0cdc1830602d91c4816a07a0db5", "raw_response_chars": 6350}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "full-text-reading-v1", "prompt_hash": "3bfddd2f05cad42e0e19226b7934e5dfb030d0dbd67db0e3403f954715aca86d", "source_hash": "6f9810fe91bf2a45614e234d57513e9050e672dc8eec96a2e1fd1a89e225877e", "raw_response_sha256": "sha256:f29f5ea4c609d38c18eac4673883e4c5e0bf56018811f0530af191e6dc7e559f", "raw_response_chars": 40939}], "target_providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "replication-target-v2", "prompt_hash": "78a918ff7ecaa547efabb78f4745fc24867d03d532007134de210e43b17f809c", "source_hash": "6f9810fe91bf2a45614e234d57513e9050e672dc8eec96a2e1fd1a89e225877e", "raw_response": "{\"parent_value\":0.909,\"unit\":\"AUC\",\"evidence\":\"The model demonstrated superior predictive accuracy compared to traditional clinical parameters, with area under the curve (AUC) values of 0.819, 0.825, and 0.832 for 1-, 2-, and 3-year survival predictions in the training set, and 0.909 in the independent GEO validation cohort (GSE71014).\",\"target_result\":\"Independent GEO validation cohort area under the curve (AUC).\",\"number_stated\":true}", "reading": {"parent_value": 0.909, "unit": "AUC", "evidence": "The model demonstrated superior predictive accuracy compared to traditional clinical parameters, with area under the curve (AUC) values of 0.819, 0.825, and 0.832 for 1-, 2-, and 3-year survival predictions in the training set, and 0.909 in the independent GEO validation cohort (GSE71014).", "target_result": "Independent GEO validation cohort area under the curve (AUC).", "number_stated": true, "source_locator": "chars 885-1175 of source sha256 6f9810fe91bf2a45614e234d57513e9050e672dc8eec96a2e1fd1a89e225877e"}, "recorded_at": "2026-10-31T15:33:48.814683+00:00"}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "replication-target-v2", "prompt_hash": "ba42af34569b8ad189aee04838d00e4a01c6cd2179699cfed8ecf1b831b57c80", "source_hash": "6f9810fe91bf2a45614e234d57513e9050e672dc8eec96a2e1fd1a89e225877e", "raw_response": "<think>We need answer JSON only. Need interpret registered target: \"the headline numerical result the paper states for its main finding\" likely AUC? Source abstract says model predictive accuracy AUC values 0.819, 0.825, 0.832 for 1,2,3-year training, and 0.909 independent validation. They ask single numerical result replication registered target has to reproduce. Which is headline numerical result? Need infer perhaps main finding is 13-gene prognostic signature, number 13? Registered target phrased generic. Need identify single numerical result paper states for main finding. They likely want headline number perhaps \"13\" genes? Let's parse wording: \"REGISTERED TARGET: the headline numerical result the paper states for its main finding\". We need extract single numerical result. Main finding abstract: identified 13 genes and constructed prognostic risk model; model AUC values. Which is headline numerical result? Could be 13-gene exosome-based prognostic signature. The question says value paper itself states for registered target. \"the headline numerical result the paper states for its main finding\" perhaps target is target quantity unknown semantic. We need decide.\n\nSource conclusion: \"successfully constructed an AML risk scoring system incorporating 13 key genes...\" Main finding is model with 13 key genes. Abstract final: \"robust 13-gene exosome-based prognostic signature\". If replication has to reproduce headline numerical result, likely 13 genes. Unit perhaps \"genes\". Evidence exact contiguous quote clause: \"This study establishes a robust 13-gene exosome-based prognostic signature for AML risk stratification and identifies novel immunomodulatory mechanisms mediated by exosome-driven Treg polarization.\" Could parent_value 13, unit \"genes\" (paper prints \"13-gene\"; unit maybe genes). target_result \"The paper states that it established a robust 13-gene exosome-based prognostic signature for AML risk stratification.\" Need headline number only? They say report headline number only, but schema includes target_result. Could target_result concise.\n\nAlternatively AUC perhaps headline numerical result: \"0.909\" independent validation, as strongest headline metric. There are multiple AUCs though single target ambiguity. Registered target says main finding, perhaps model performance, likely AUC. They intentionally say \"single numerical result\". Main finding maybe \"the model demonstrated superior predictive accuracy...\" headline numerical result could be AUC. Which one? Need use contextual clues. \"a replication of the registered target has to reproduce\" Maybe this is from benchmark where target variable (\"parent_value\") perhaps target entity. They give vague registered target, and expect extract headline number. Often tasks ask \"What is the main numerical result reported in abstract?\" The answer likely one value such as AUC = 0.909. Need reason.\n\nThe source abstract has many numbers: cohorts n=151, n=337, 13 genes, AUC 0.819/0.825/0.832, 0.909 validation, HR 0.22, CI, docking score. \"headline numerical result ... main finding\" Main finding likely model predictive performance. The headline performance number is AUC 0.909 in independent validation. But independent validation AUC 0.909 without horizon? The sentence: \"with area under the curve (AUC) values of 0.819, 0.825, and 0.832 for 1-, 2-, and 3-year survival predictions in the training set, and 0.909 in the independent GEO validation cohort (GSE71014).\" This links 0.909 directly to validation cohort. If target registered as \"headline numerical result paper states for main finding\", perhaps 0.909 is likely.\n\nCould be 13 as headline because title/main contribution \"13-gene model\". The target definition uses singular \"headline numerical result the paper states for its main finding\", and asks headline number only. Often these prompts are generated to extract a specific \"parent_value\" from paper, where registered target may be described semantically by benchmark creator. They might intend \"number of genes in model\" as main finding. The phrase \"the paper itself states for the registered target\" generic boilerplate. We need infer target from source.\n\nLet's inspect exact wording. Title: \"Construction of a prognostic risk model for acute myeloid leukemia based on exosomal genes and analysis of immune microenvironment characteristics\". Abstract says \"we identified 13 ... and constructed a prognostic risk model.\" Main result is 13-gene signature. Headline number might be 13 genes. If asking AUC, they'd perhaps register \"the model's area under the curve in the independent validation cohort\". But they didn't. Registered target explicitly \"the headline numerical result the paper states for its main finding\" perhaps designed to let us identify central reported metric. Usually headline numerical result is 13? A paper title doesn't say 13. Conclusion does. Abstract’s first results: 13 genes. The model performance is 0.909.\n\nCould target_result need mention number and unit. \"headline number only\" perhaps no extra numerical values in evidence? But evidence exact quote may contain only target number. If 13, quote contains 13. If 0.909, quote clause contains other AUCs. They say \"When that is how the text links them, quote the whole clause so a reader can check the attribution\". Could quote whole clause with list. The request says \"find the single numerical result ... Report the headline number only ... evidence exact contiguous quotation ... contains the number; do not shorten it\". This suggests maybe target is part of a list, and evidence should preserve attribution. Could be 0.909 attached via \"and\". Or 13 attached to genes directly.\n\n\"single numerical result\" maybe asks replicate target, likely one number. If quoting AUC sentence, whole sentence includes many numbers but parent_value one. Fine.\n\nLet's search patterns mentally from similar benchmark prompts. They often say:\n\"Read supplied paper text and find the single numerical result that a replication of the registered target has to reproduce. Report the headline number only: the value the paper itself states for the registered target, not a value you calculated, converted, averaged or reconstructed. The unit must be the unit the paper prints. ... REGISTERED TARGET: [specific thing]\"\nHere registered target sometimes generic phrase. Need output exact source. This one perhaps target is \"the headline numerical result ... main finding\" and expected likely number of selected genes. The source has a target article whose central result is \"13-gene risk signature\". Replication would need reproduce 13 genes, whereas AUC is secondary validation metric. Yet \"headline numerical result\" perhaps central metric 0.909.\n\nCould be \"main finding\" = \"prognostic risk model based on exosomal genes\", and numerical result associated = \"13 genes\". The unit printed = \"genes\". The conclusion specifically: \"This study establishes a robust 13-gene exosome-based prognostic signature...\" That's very headline-like. The abstract's final conclusion same. The model performance values are several, not \"single\". Thus only unambiguous singular headline number tied to main contribution is 13. I'd lean 13.\n\nLet's ensure exact quote contiguous. Could use: \"This study establishes a robust 13-gene exosome-based prognostic signature for AML risk stratification and identifies novel immunomodulatory mechanisms mediated by exosome-driven Treg polarization.\" This is exact. Unit should paper prints: perhaps \"genes\" based on \"13-gene\". parent_value 13. target_result could be \"13 genes\" or \"The study established a 13-gene exosome-based prognostic signature for AML risk stratification.\" But \"Report headline number only\" maybe target_result should say \"13 genes\" only, though evidence also required. They likely expect target_result description. Schema demands string. Could set \"13 genes\". The evidence quote necessarily text. number_stated true.\n\nWould unit be \"genes\" or \"exosome-associated genes\"? The printed unit is \"13-gene\"; likely unit \"genes\". They say unit must be unit paper prints. Use \"genes\". Exact quote includes \"13-gene\". Good.\n\nNeed valid JSON no markdown. Ensure en dash etc okay. Let's answer.</think>\n\n{\"parent_value\":13,\"unit\":\"genes\",\"evidence\":\"This study establishes a robust 13-gene exosome-based prognostic signature for AML risk stratification and identifies novel immunomodulatory mechanisms mediated by exosome-driven Treg polarization.\",\"target_result\":\"13 genes\",\"number_stated\":true}", "reading": {"parent_value": 13, "unit": "genes", "evidence": "This study establishes a robust 13-gene exosome-based prognostic signature for AML risk stratification and identifies novel immunomodulatory mechanisms mediated by exosome-driven Treg polarization.", "target_result": "13 genes", "number_stated": true, "source_locator": "chars 1885-2082 of source sha256 6f9810fe91bf2a45614e234d57513e9050e672dc8eec96a2e1fd1a89e225877e"}, "recorded_at": "2026-10-31T15:33:59.773947+00:00"}], "agreement": false, "disagreements": {"target_result": {"minimax": "The 13-gene exosome-based prognostic model achieved AUC values of 0.819, 0.825, and 0.832 for 1-, 2-, and 3-year survival predictions in the training set and 0.909 in the independent GEO validation cohort (GSE71014).", "minimax_alt": "The model demonstrated superior predictive accuracy compared to traditional clinical parameters, with area under the curve (AUC) values of 0.819, 0.825, and 0.832 for 1-, 2-, and 3-year survival predictions in the training set, and 0.909 in the independent GEO validation cohort (GSE71014).", "decisive": false}, "analysis_methods": {"minimax": ["univariate Cox regression", "least absolute shrinkage and selection operator (LASSO)", "receiver operating characteristic (ROC) curve analysis", "Kaplan-Meier analysis with log-rank test", "univariate and multivariate Cox regression analyses", "Wilcoxon rank-sum test", "Benjamini-Hochberg false discovery rate correction", "gene ontology (GO) enrichment analysis", "Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis", "CIBERSORTx immune-cell infiltration analysis", "STRING protein-protein interaction network analysis", "DSigDB drug enrichment analysis", "molecular docking"], "minimax_alt": ["univariate Cox regression", "multivariate Cox regression", "least absolute shrinkage and selection operator (LASSO)", "ROC curve analysis (AUC)", "Kaplan-Meier analysis with log-rank test", "Wilcoxon rank-sum test", "Benjamini-Hochberg false discovery rate correction", "CIBERSORTx", "GO and KEGG pathway enrichment analysis", "STRING protein-protein interaction network analysis", "DSigDB drug enrichment analysis", "molecular docking"], "decisive": false}, "statistical_method": {"minimax": "receiver operating characteristic (ROC) curve analysis", "minimax_alt": "ROC curve analysis (area under the curve [AUC])", "decisive": true}, "parent_value": {"minimax": 0.909, "minimax_alt": 13, "decisive": true}, "unit": {"minimax": "AUC", "minimax_alt": "genes", "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:f51938cef1f597e760353b1ce1b436ce58301b1c12b646eeaffeeee5f915f101", "type": "sep_package", "label": "SEP v1 package for pmid:40890357"}], "activities": [{"id": "activity:conversion:pmid:40890357", "type": "conversion", "used": ["package:f51938cef1f597e760353b1ce1b436ce58301b1c12b646eeaffeeee5f915f101"], "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": "pmid:40890357", "parent_hash": "sha256:6668e7675fd035141ec29cfd5b4db0dbf010788fa96189563173d7e0a7249933", "fork_of": null, "generation": 2}, "gaps": ["abstract", "asset:source:pmid:40890357 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:40890357' does not resolve to a declared asset", "targets"]}