{"id":"W4408776808","doi":"10.1016/j.labinv.2024.103175","title":"941 OCEAN Challenge: Advancing AI for Generalized Ovarian Cancer Diagnosis","year":2025,"lang":"en","type":"article","venue":"Laboratory Investigation","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary; Vancouver General Hospital; Canadian Centre for Applied Research in Cancer Control; BC Cancer Agency; BC Cancer Foundation; University of British Columbia","funders":"","keywords":"Ovarian cancer; Cancer; Medicine; Computational biology; Internal medicine; Biology","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":[0.0003879634,0.0001422322,0.0002529643,0.0001436657,0.0001622443,0.00003534833,0.00007670447,0.0001033564,0.00004691441],"category_scores_gemma":[0.0009287625,0.0001349865,0.00005557796,0.0004027573,0.00009630155,0.000135033,0.00002041526,0.0002707757,0.000002690296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001504674,"about_ca_system_score_gemma":0.0005006321,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008887118,"about_ca_topic_score_gemma":0.00002138439,"domain_scores_codex":[0.9989726,0.00006624747,0.0002707598,0.0002867491,0.0001608363,0.0002427964],"domain_scores_gemma":[0.9991335,0.0001067219,0.00009163922,0.0002119752,0.0002738377,0.0001823546],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002103773,0.000206979,0.5601836,0.002065454,0.0004274189,0.00002855069,0.001778104,0.001409674,0.03987051,0.06693774,0.27698,0.04990153],"study_design_scores_gemma":[0.009801915,0.0002705247,0.05641412,0.002050075,0.000926341,0.0000038335,0.0002482342,0.09760445,0.04795804,0.01649483,0.7675613,0.0006663845],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8346732,0.002960213,0.01081433,0.1481798,0.001374369,0.0009935898,0.00005753952,0.0002756207,0.0006713548],"genre_scores_gemma":[0.9121282,0.0009706991,0.01929542,0.06449463,0.001060626,0.0008483558,0.000217992,0.00007181439,0.0009122412],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5037696,"threshold_uncertainty_score":0.5504591,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01225138466762397,"score_gpt":0.3204720121160677,"score_spread":0.3082206274484437,"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."}}