{"id":"W4402282873","doi":"10.1016/j.media.2024.103342","title":"ATEC23 Challenge: Automated prediction of treatment effectiveness in ovarian cancer using histopathological images","year":2024,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"AI in cancer detection","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"National Taiwan University of Science and Technology; Tri-Service General Hospital; National Science and Technology Council","keywords":"Bevacizumab; Medicine; Ovarian cancer; Perforation; Oncology; Cancer; Internal medicine; Adverse effect; Medical physics; Chemotherapy","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":[0.002045032,0.001912497,0.001711641,0.002646869,0.0004995872,0.00135154,0.002019854,0.00249214,0.003907493],"category_scores_gemma":[0.006291464,0.0003299397,0.001871192,0.001091234,0.0003128979,0.0006302864,0.000974293,0.001031325,0.002651852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008789192,"about_ca_system_score_gemma":0.001773273,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.020725,"about_ca_topic_score_gemma":0.02404985,"domain_scores_codex":[0.9987286,0.0003156962,0.00008578203,0.0003399662,0.0003865075,0.0001434796],"domain_scores_gemma":[0.9978883,0.001094567,0.0001336711,0.0002662398,0.0003992653,0.0002179553],"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":[0.003135814,0.001777426,0.05863455,0.001927739,0.00163081,0.0009467234,0.0000924786,0.05656178,0.01724528,0.0007226655,0.282918,0.5744067],"study_design_scores_gemma":[0.001124123,0.002326427,0.06964187,0.0003211942,0.001019684,0.002823728,0.0002814802,0.8143057,0.0355944,0.003115181,0.06924412,0.0002021158],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6387985,0.01801814,0.06170275,0.004665627,0.001663431,0.002201021,0.2293653,0.03187749,0.01170768],"genre_scores_gemma":[0.655446,0.002167791,0.09580167,0.00139223,0.0006246208,0.0009045796,0.2305109,0.0008835196,0.01226877],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.020725,"threshold_uncertainty_score":0.04120874,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0252842723934936,"score_gpt":0.3321385277453133,"score_spread":0.3068542553518197,"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."}}