Prognostic and Predictive Value of Machine Learning-Based Biomarker and Pathomics Signatures in Patients With Prostate Cancer.

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"capabilities": [], "data_gaps": ["source:pmid:40676748"]}, "gaps": ["corpus_record_absent"]}, "review": {"state": "partial", "machine_critique": {"status": "passed", "providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "full-text-reading-v1", "prompt_hash": "a0661ffbf8bd41ef875e78a8f73c0b0bbccb342e6ce579a3d3a44c5f9bc0e0ad", "source_hash": "25aeb25ad90acef0834ce98aef9e19e37bd90b4a1a322aa6869d1b2334c29661", "raw_response_sha256": "sha256:ba17dda7ccde35f6cd72c88a5a4c992cb2ff1c5d484b18c7a6bf3373597961ca", "raw_response_chars": 5565}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "full-text-reading-v1", "prompt_hash": "e10893fcd863d8c357f8ab4eb58deeead90ab444c4e6645f9fa17d2c4492d9a1", "source_hash": "25aeb25ad90acef0834ce98aef9e19e37bd90b4a1a322aa6869d1b2334c29661", "raw_response_sha256": "sha256:d6b6e3408faac7396696fb8406fac46ff76252c0af4349f00d29385231e2c18a", "raw_response_chars": 52121}], "target_providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "replication-target-v2", "prompt_hash": "26eb6f2960ab58cde816187d1083fc519309c771504689c1766bdc14db04c2b0", "source_hash": "25aeb25ad90acef0834ce98aef9e19e37bd90b4a1a322aa6869d1b2334c29661", "raw_response": "{\"parent_value\":0,\"unit\":\"\",\"evidence\":\"\",\"target_result\":\"The paper states that pathology-based evaluations of MAPRS, PSpc, and their combination effectively predicted recurrence-free survival in patients who underwent radical prostatectomy, but it does not state a headline numerical result for this main finding.\",\"number_stated\":false}", "reading": {"parent_value": 0, "unit": "", "evidence": "", "target_result": "The paper states that pathology-based evaluations of MAPRS, PSpc, and their combination effectively predicted recurrence-free survival in patients who underwent radical prostatectomy, but it does not state a headline numerical result for this main finding.", "number_stated": false, "source_locator": null}, "recorded_at": "2026-10-31T15:21:12.077067+00:00"}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "replication-target-v2", "prompt_hash": "ece3510fd0decaf2a35ff1faa2da36b3eb14b6507531cdbab69b0de535df0df8", "source_hash": "25aeb25ad90acef0834ce98aef9e19e37bd90b4a1a322aa6869d1b2334c29661", "raw_response": "<think>We need answer JSON only. Need interpret request. We have source text likely abstract/introduction no headline numerical result for main finding. Need find single numerical result registered target headline numerical result paper states for main finding. Source contains five algorithms, ten pathomics algorithms, dates, trial number, maybe no performance metric. We must extract claims only supplied. If no number for registered target, number_stated false, parent_value 0, unit/evidence empty, target_result say what text states instead. Need exact contiguous quotation evidence empty because no numeric result. target_result probably describe source states MAPRS etc. Need ensure perhaps \"Five machine learning algorithms\" is numerical result? Registered target \"headline numerical result the paper states for its main finding\" asks single numerical result. Source text has \"Five machine learning algorithms...\" but that's study methods, not headline numerical result. Could target be title/abstract and registered target ambiguous. Need answer false. Need target_result wording perhaps \"The text states that MAPRS correlated with poorer recurrence-free survival ... pathology-based evaluations ... predicted RFS...\". We need perhaps include exact claim? target_result can paraphrase? Instruction says \"say in target_result what the text does state instead.\" We can summarize. Evidence must empty when false. JSON valid.\n\nNeed be careful \"the headline numerical result the paper states for its main finding\" maybe target itself is broad and paper's headline number could be \"Five machine learning algorithms\". But numerical result needs reproduce likely effect sizes/AUC etc. There are no results. Trial registration number not result. \"Five machine learning algorithms\" methodology count, and list pathomics has 10. Not main finding. So false.\n\nCould target_result state no numerical result; qualitative claims: MAPRS correlated with poorer RFS and pathology-based evaluations effectively predicted RFS. Maybe mention no headline numerical value is provided. Exact text quotes? Evidence empty mandated. target_result isn't necessarily evidence. Since claims only source. Say: \"No headline numerical result is stated. The text instead reports that the MAPRS correlated with poorer recurrence-free survival and that pathology-based evaluations of MAPRS, PSpc, and their combination effectively predicted recurrence-free survival after radical prostatectomy.\" All sourced.\n\nNeed number parent 0, unit \"\". JSON object no markdown.</think>\n\n{\"parent_value\":0,\"unit\":\"\",\"evidence\":\"\",\"target_result\":\"No headline numerical result is stated. The text instead reports that the MAPRS correlated with poorer recurrence-free survival and that pathology-based evaluations of MAPRS, PSpc, and their combination effectively predicted recurrence-free survival after radical prostatectomy.\",\"number_stated\":false}", "reading": {"parent_value": 0, "unit": "", "evidence": "", "target_result": "No headline numerical result is stated. The text instead reports that the MAPRS correlated with poorer recurrence-free survival and that pathology-based evaluations of MAPRS, PSpc, and their combination effectively predicted recurrence-free survival after radical prostatectomy.", "number_stated": false, "source_locator": null}, "recorded_at": "2026-10-31T15:21:15.504056+00:00"}], "agreement": true, "disagreements": {"target_result": {"minimax": "unknown (the supplied text states no headline numerical result)", "minimax_alt": "unclear (the supplied text states no headline numerical result)", "decisive": false}, "physical_methods": {"minimax": ["Hematoxylin and eosin (H&E)-stained digital pathology", "In-house histopathological samples"], "minimax_alt": ["hematoxylin and eosin (H&E)‐stained digital pathology", "in‐house histopathological samples"], "decisive": false}, "analysis_methods": {"minimax": ["LASSO", "RSF", "SVM-RFE", "Boruta", "XGBoost", "GBM", "plsRCox", "CoxBoost", "Enet", "Ridge", "SVM", "superPC"], "minimax_alt": ["biomarker identification using LASSO, RSF, SVM‐RFE, Boruta, and XGBoost", "MAPRS construction and validation", "PSpc derivation using XGBoost, RSF, GBM, plsRCox, CoxBoost, Enet, Ridge, LASSO, SVM, and superPC", "pathology‐based evaluation of MAPRS, PSpc, and their combination", "MAPRS combined with clinical indicators for progression‐free survival prediction"], "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; 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.passed"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "package:1a0fb8acc904f57e2b7230844c2a2e811951404e025d709fe070c8dfd702c504", "type": "sep_package", "label": "SEP v1 package for pmid:40676748"}], "activities": [{"id": "activity:conversion:pmid:40676748", "type": "conversion", "used": ["package:1a0fb8acc904f57e2b7230844c2a2e811951404e025d709fe070c8dfd702c504"], "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": "488441b0-a7a3-4124-938a-1fd05c90010c", "parent_hash": "1a0fb8acc904f57e2b7230844c2a2e811951404e025d709fe070c8dfd702c504", "fork_of": null, "generation": 1}, "gaps": ["abstract", "asset:source:pmid:40676748 not verified", "authors", "capability", "corpus_record_absent", "execution.not_run", "method", "review.human_critique", "review.machine_critique.passed", "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:40676748' does not resolve to a declared asset", "targets"]}