{"id":"W4324136996","doi":"10.1200/jco.2023.41.6_suppl.299","title":"Patient-level data meta-analysis of a multi-modal artificial intelligence (MMAI) prognostic biomarker in high-risk prostate cancer: Results from six NRG/RTOG phase III randomized trials.","year":2023,"lang":"en","type":"article","venue":"Journal of Clinical Oncology","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Saint John Regional Hospital","funders":"","keywords":"Medicine; Prostate cancer; Biomarker; Oncology; Internal medicine; Radiation therapy; Cohort; Randomized controlled trial; Prostate; Cumulative incidence; Cancer","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.05755393,0.0002869816,0.01088101,0.0009516011,0.0000546565,0.00002532273,0.0005493459,0.0003898549,0.0002664264],"category_scores_gemma":[0.2878852,0.0001746809,0.002703137,0.001499928,0.0006446138,0.0001554254,0.0002708482,0.00194057,0.000008913937],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008219919,"about_ca_system_score_gemma":0.001517499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004177145,"about_ca_topic_score_gemma":0.0008126065,"domain_scores_codex":[0.9790809,0.006519669,0.01245818,0.0006436431,0.0008833386,0.000414283],"domain_scores_gemma":[0.9288459,0.05905385,0.01018949,0.000741854,0.0006409016,0.0005280057],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.3191654,0.003349527,0.003124302,0.00002256518,0.1086185,0.0009987922,0.0007678174,0.003683761,0.0002524524,0.00003079565,0.001184121,0.5588019],"study_design_scores_gemma":[0.215495,0.002108118,0.01005389,0.0001017793,0.2622073,0.0000123276,0.0002561728,0.5069591,0.00006306266,0.001018023,0.00152004,0.0002051521],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9671418,0.001628668,0.01064566,0.01598361,0.001776174,0.00127782,0.001497952,0.00002374968,0.00002454649],"genre_scores_gemma":[0.9792789,0.003518631,0.01569002,0.0005642864,0.0004077316,0.0000357093,0.0004235857,0.00002829525,0.0000528101],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5585968,"threshold_uncertainty_score":0.9704465,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4715789308372614,"score_gpt":0.5536479833131753,"score_spread":0.08206905247591395,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}