{"id":"W7133363530","doi":"","title":"Time-to-Event Pretraining for 3D Medical Imaging.","year":2025,"lang":"en","type":"article","venue":"PubMed","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Context (archaeology); Medical imaging; Benchmark (surveying); Medical record; Medical diagnosis; Scalability; Disease","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":[],"consensus_categories":[],"category_scores_codex":[0.001338329,0.0001003622,0.0001449288,0.0001534661,0.0001151782,0.00008235267,0.0009623462,0.00005380972,0.00004770971],"category_scores_gemma":[0.002003724,0.00009670624,0.0000540137,0.0003809397,0.0000283249,0.0001006962,0.0003460929,0.0002034297,0.00005799541],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008101778,"about_ca_system_score_gemma":0.0001580493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001763528,"about_ca_topic_score_gemma":0.000003525224,"domain_scores_codex":[0.9984372,0.00008584855,0.0002302208,0.0003983252,0.0003405834,0.0005078019],"domain_scores_gemma":[0.9988208,0.0003662471,0.00004510181,0.0004226732,0.00007681802,0.000268389],"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":[0.000004458349,0.00001320667,0.001414729,0.00004086524,0.000007337344,0.000003978526,0.0001880837,0.00006904483,3.561763e-7,0.003190395,0.01388296,0.9811846],"study_design_scores_gemma":[0.000333564,0.00001281282,0.04901563,0.0000424047,0.000004275226,0.000006516289,0.00000754758,0.6778702,0.00001364436,0.0009080205,0.2716448,0.0001406266],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0009003899,0.0002117478,0.8904423,0.08996296,0.0009855818,0.001350316,0.000002828004,0.000455048,0.01568886],"genre_scores_gemma":[0.7453024,0.000005285259,0.1821201,0.03524878,0.00071585,0.00982486,0.00001275329,0.00004877659,0.02672123],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9810439,"threshold_uncertainty_score":0.3943566,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0125268303573284,"score_gpt":0.2880002017188458,"score_spread":0.2754733713615174,"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."}}