{"id":"W4308639157","doi":"10.1002/jso.27137","title":"Using a machine learning algorithm to predict outcome of primary cytoreductive surgery in advanced ovarian cancer","year":2022,"lang":"en","type":"article","venue":"Journal of Surgical Oncology","topic":"Ovarian cancer diagnosis and treatment","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; Princess Margaret Cancer Centre; Hospital for Sick Children; University of Toronto","funders":"","keywords":"Medicine; Retrospective cohort study; Recursive partitioning; Ascites; Cohort; Algorithm; Surgery; Ovarian cancer; Stage (stratigraphy); Internal medicine; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003271409,0.0006403655,0.0006266829,0.0009214424,0.0002713243,0.0005418701,0.0005072991,0.0006597246,0.0008376796],"category_scores_gemma":[0.009850828,0.0001756417,0.0006622981,0.0003109738,0.0002209994,0.0004536239,0.0004321519,0.0006481947,0.0003581745],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005341073,"about_ca_system_score_gemma":0.0009394796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002864718,"about_ca_topic_score_gemma":0.002296429,"domain_scores_codex":[0.9991905,0.0003884014,0.00005923937,0.0001675146,0.0001236321,0.000070791],"domain_scores_gemma":[0.9968632,0.002214029,0.0003445703,0.00009200126,0.0003925696,0.00009356592],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001239575,0.0007771362,0.3450702,0.0001196551,0.0005120873,0.0001845205,0.00009501488,0.3369203,0.002175208,0.0005548499,0.00443037,0.307921],"study_design_scores_gemma":[0.00008069516,0.0004435135,0.01832214,0.00003976042,0.00006233195,0.0001400218,0.00002209365,0.9786239,0.0008531687,0.000913767,0.0004857147,0.00001287682],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7431282,0.001278704,0.2491815,0.00133753,0.0001483136,0.0003554074,0.0009520029,0.001251178,0.002367049],"genre_scores_gemma":[0.9499221,0.0001827736,0.04786752,0.0001810179,0.00008316097,0.0001790197,0.0009905996,0.00002762533,0.0005662763],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003271409,"threshold_uncertainty_score":0.01730108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05554470023616943,"score_gpt":0.3630833137670265,"score_spread":0.307538613530857,"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."}}