{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001710061,0.001230679,0.0006755599,0.0008208147,0.0004434963,0.0006856361,0.001796715,0.001437361,0.003385186],"category_scores_gemma":[0.006214969,0.0007479769,0.001161989,0.0006663208,0.0004293916,0.0008447373,0.00121205,0.002907719,0.002628026],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006823042,"about_ca_system_score_gemma":0.001809605,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009466507,"about_ca_topic_score_gemma":0.02775454,"domain_scores_codex":[0.9994432,0.0001636878,0.00003761317,0.0001828827,0.0001181224,0.00005442414],"domain_scores_gemma":[0.9980387,0.001280488,0.0001500557,0.0002280108,0.0002088527,0.00009401311],"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":[0.0007783942,0.0007256345,0.01650016,0.000528475,0.0005470423,0.0005043143,0.0002049883,0.2207433,0.00826648,0.002673317,0.07199726,0.6765307],"study_design_scores_gemma":[0.00005044373,0.000136758,0.002817109,0.00005207422,0.00005892128,0.0002326416,0.00002485797,0.9850141,0.003626241,0.002998369,0.004960884,0.00002751642],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.085893,0.004646812,0.8710809,0.001671778,0.0007748945,0.0004728621,0.006194001,0.024771,0.004494725],"genre_scores_gemma":[0.5752022,0.0019231,0.3828036,0.001637473,0.0004196964,0.0008843913,0.02586059,0.0009417187,0.01032726],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009466507,"threshold_uncertainty_score":0.01882279,"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."}}