Development and validation of a biomarker-based prediction model for metastasis in patients with colorectal cancer: Application of machine learning algorithms.

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[{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "replication-target-v2", "prompt_hash": "2fa27f2ada863dcfb283919484eb06f9f597effed0ed296031d9bf0b96789ef8", "source_hash": "ddc20ebfa8efe9bf54f38a60c7ec373a52e5df46db79e7a65d90978dbd9544de", "raw_response": "{\"parent_value\":0.99,\"unit\":\"AUC\",\"evidence\":\"The RF model exhibited an accuracy of 0.97, a kappa value of 0.92, and an area under the curve (AUC) of 0.99 in the external validation dataset.\",\"target_result\":\"The random forest model's external-validation area under the curve was 0.99.\",\"number_stated\":true}", "reading": {"parent_value": 0.99, "unit": "AUC", "evidence": "The RF model exhibited an accuracy of 0.97, a kappa value of 0.92, and an area under the curve (AUC) of 0.99 in the external validation dataset.", "target_result": "The random forest model's external-validation area under the curve was 0.99.", "number_stated": true, "source_locator": "chars 1651-1795 of source sha256 ddc20ebfa8efe9bf54f38a60c7ec373a52e5df46db79e7a65d90978dbd9544de"}, "recorded_at": "2026-10-31T15:21:39.747099+00:00"}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "replication-target-v2", "prompt_hash": "3089b970ced4f88143445b4adb12ea9cf14153a782e5e0391305ef81e90b0720", "source_hash": "ddc20ebfa8efe9bf54f38a60c7ec373a52e5df46db79e7a65d90978dbd9544de", "raw_response": "<think>We need answer JSON object only. Need identify single numerical result replication registered target main finding headline number paper states. Need likely AUC 0.99, perhaps headline numerical result? Need parse instruction carefully: REGISTERED TARGET: \"the headline numerical result the paper states for its main finding\". Need report headline number only, value paper itself states, unit as printed. Evidence exact contiguous quote containing number. Could be RF model external validation with accuracy .97 kappa .92 AUC .99. Which single is headline? Main finding likely AUC 0.99? Need infer benchmark extraction expectation. They ask parent_value perhaps headline numerical result. We need choose likely 0.99, unit maybe \"AUC\" (printed as area under curve (AUC) of 0.99). Evidence full clause: \"The RF model exhibited an accuracy of 0.97, a kappa value of 0.92, and an area under the curve (AUC) of 0.99 in the external validation dataset.\" But report headline number only means parent_value 0.99. Unit string \"AUC\" perhaps. target_result concise says external validation AUC of 0.99. Evidence exact contiguous quote. Could they consider main result as accuracy 0.97? Let's deeply inspect wording.\n\nQuestion generated perhaps designed to extract target numerical value from abstract. Registered target generic. \"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: \"the headline numerical result the paper states for its main finding\". In study abstract, final result sentence lists three performance measures. Headline likely AUC of 0.99. Could be accuracy because main model highest accuracy, but headline result often external validation metrics and AUC strongest. Need perhaps target result expected 0.97? Let's understand source likely paper and maybe title/abstract. The \"headline numerical result\" could be 0.99 as AUC. We need ensure unit exactly paper prints. \"area under the curve (AUC)\" perhaps unit is \"AUC\" not unit technically dimensionless. They require unit as paper prints. Could set unit \"AUC\". Evidence exact quote sentence. target_result perhaps \"External-validation AUC of 0.99 for the RF model.\" number_stated true.\n\nCould number be 16 key genes? Main finding is identified 16 key genes. Replication target might reproduce 16 biomarkers rather than performance. Yet \"headline numerical result the paper states for its main finding\" likely intended to select result metric. Let's inspect prompt wording perhaps from a benchmark where target entity is registered by question, and source gives multiple numbers. They want \"single numerical result that a replication ... has to reproduce.\" The registered target is defined abstractly here. Maybe headline number in paper's result is expected based on abstract: \"The RF model exhibited an accuracy of 0.97, a kappa value of 0.92, and an area under the curve (AUC) of 0.99 in external validation dataset.\" They ask single headline numerical result; likely AUC 0.99 because often headline performance metric. They emphasize attribution may be via list / respectively. Perhaps target could have been something like \"the headline numerical result ...\". Need choose.\n\n\"Report the headline number only: the value the paper itself states for the registered target\" Means answer fields parent_value etc. Parent_value likely numeric target value. Unit perhaps \"%\" if accuracy. Source prints accuracy = 0.97 not percent. AUC 0.99 no unit. Their schema requires string unit, could be empty? But if number stated, probably unit \"AUC\". Prompt: \"The unit must be the unit the paper prints.\" Here no physical unit. We can use \"AUC\" as metric label. Evidence whole clause if linked via words naming target. Exact quote can be sentence.\n\nCould target_result state \"The RF model's external-validation area under the curve (AUC) was 0.99.\" That's paraphrase but target_result not evidence. Fine.\n\nNeed JSON valid, no markdown. Ensure evidence exact contiguous. Parent value numeric 0.99.\n\nLet's not rush—could \"headline number\" be \"16 key genes\". Let's inspect abstract flow:\nPurpose.\nMethods.\nResults: Among 22,283 registered genes ... identified 16 key genes ... On internal validation ... metrics. Top five biomarkers ... RF model external validation accuracy .97, kappa .92, AUC .99.\nConclusion.\n\nMain finding is biomarker-based prediction model; headline numerical result perhaps model AUC. Usually when reporting classification model, headline result = AUC 0.99. The prompt singular suggests select one likely final/most salient metric. We need perhaps use exact full clause. We'll answer.\n\nNeed decide unit. \"area under the curve (AUC)\" perhaps unit string could be \"AUC\". The paper prints \"an area under the curve (AUC) of 0.99\", no unit. If unit must be unit paper prints, metric label qualifies. Could put \"AUC\" rather than \"area under the curve (AUC)\". Which exact printed term? \"area under the curve (AUC)\" is printed, but unit should likely \"AUC\". Better unit \"AUC\" because recognized metric. Yet perhaps schema expects empty if dimensionless? Prompt says leave unit empty only if no number. They likely expect \"AUC\" or \"%\". We can state \"AUC\". Evidence sentence exact.\n\n\"target_result\": should perhaps be \"The RF model achieved an AUC of 0.99 in the external validation dataset.\" This is clear. \"headline number only\" means only one number in target_result, but evidence necessarily contains all three numbers due full sentence. Does that violate headline number only? They explicitly ask quote whole clause for attribution; evidence may include multiple numbers. Fine. Could quote narrower exact contiguous fragment: \"an area under the curve (AUC) of 0.99 in the external validation dataset.\" Does that contain target attribution? It doesn't identify RF model, but metric and external validation. To let reader check attribution, whole sentence better. They say \"When that is how the text links them, quote the whole clause so a reader can check the attribution for themselves.\" So full sentence.\n\nCould quote \"The RF model exhibited an accuracy of 0.97, a kappa value of 0.92, and an area under the curve (AUC) of 0.99 in the external validation dataset.\" Exact. Great.\n\nLet's answer JSON only.</think>\n\n{\"parent_value\":0.99,\"unit\":\"AUC\",\"evidence\":\"The RF model exhibited an accuracy of 0.97, a kappa value of 0.92, and an area under the curve (AUC) of 0.99 in the external validation dataset.\",\"target_result\":\"The RF model achieved an AUC of 0.99 in the external validation dataset.\",\"number_stated\":true}", "reading": {"parent_value": 0.99, "unit": "AUC", "evidence": "The RF model exhibited an accuracy of 0.97, a kappa value of 0.92, and an area under the curve (AUC) of 0.99 in the external validation dataset.", "target_result": "The RF model achieved an AUC of 0.99 in the external validation dataset.", "number_stated": true, "source_locator": "chars 1651-1795 of source sha256 ddc20ebfa8efe9bf54f38a60c7ec373a52e5df46db79e7a65d90978dbd9544de"}, "recorded_at": "2026-10-31T15:21:46.855095+00:00"}], "agreement": false, "disagreements": {"analysis_methods": {"minimax": ["differential expression analysis", "redundancy analysis", "recursive feature elimination", "lasso and elastic-net regularized generalized linear model (glmnet)", "k-nearest neighbors (kNN)", "support vector machine (SVM) with Radial Basis Function Kernel", "random forest (RF)", "eXtreme Gradient Boosting (XGBoost)", "10-fold cross-validation with 3 repeats"], "minimax_alt": ["Differential expression analysis using GEO2R", "Benjamini-Hochberg adjustment with a Benjamini-Hochberg adjusted P-value threshold of less than 0.05", "Correlation-based redundancy analysis using the caret findCorrelation function", "Recursive feature elimination using the caret rfFuncs implementation", "10-fold cross-validation with 3 repeats and 10 repetitions of RFE", "Lasso and elastic-net regularized generalized linear model (glmnet)", "K-nearest neighbors (kNN)", "Support vector machine (SVM) with Radial Basis Function Kernel", "Random forest (RF)", "eXtreme Gradient Boosting (XGBoost)", "External validation on GSE41568"], "decisive": false}, "statistical_method": {"minimax": "random forest (RF)", "minimax_alt": "Random forest (RF) machine-learning model", "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:814948aed846d0c278c3b4726459bf778ebebe001e91e657adf8705492c4a084", "type": "sep_package", "label": "SEP v1 package for pmid:39839508"}], "activities": [{"id": "activity:conversion:pmid:39839508", "type": "conversion", "used": ["package:814948aed846d0c278c3b4726459bf778ebebe001e91e657adf8705492c4a084"], "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:39839508", "parent_hash": "sha256:39dbd4bba97fef67d3caad48123565d6bc151027eb84530785056db2b3111456", "fork_of": null, "generation": 2}, "gaps": ["abstract", "asset:source:pmid:39839508 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:39839508' does not resolve to a declared asset", "targets"]}