{"id":"W6945125418","doi":"10.25384/sage.23732944","title":"sj-ipynb-3-mdm-10.1177_0272989X231188027 – Supplemental material for Constrained Optimization for Decision Making in Health Care Using Python: A Tutorial","year":2023,"lang":"en","type":"dataset","venue":"Sage Journals Data","topic":"SAS software applications and methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Clinical Evaluative Sciences; University of Toronto","funders":"","keywords":"Health care; Constrained optimization; Medical decision making; Clinical decision making; Constraint (computer-aided design); Process (computing); Work (physics); Decision-making models","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002023019,0.001451248,0.001216805,0.001206184,0.0005273271,0.002131859,0.002545737,0.001173705,0.3115486],"category_scores_gemma":[0.008155647,0.00118931,0.001637334,0.001652864,0.0004621531,0.001825267,0.002250214,0.002492826,0.2300285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000895538,"about_ca_system_score_gemma":0.002326609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00485033,"about_ca_topic_score_gemma":0.01199688,"domain_scores_codex":[0.99911,0.0002041865,0.00009794845,0.0002239754,0.0002653169,0.00009846393],"domain_scores_gemma":[0.9978114,0.001250903,0.000117121,0.0003840419,0.000297001,0.0001394821],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005238169,0.00002823918,0.000462791,0.0004899395,0.00003691566,0.00002304644,0.00002168424,0.001341403,0.0003356588,0.001895081,0.9825382,0.01277482],"study_design_scores_gemma":[0.0003730821,0.00003519404,0.002769616,0.0003479599,0.00003924603,0.0001168989,0.00004307772,0.01883302,0.002928587,0.02482515,0.949605,0.00008299759],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001130504,0.0004810742,0.05139274,0.001116986,0.0003371418,0.0002627053,0.7713976,0.1565122,0.01736904],"genre_scores_gemma":[0.009550788,0.0007337494,0.1133659,0.001956694,0.0001685365,0.002713177,0.7871699,0.06363546,0.02070585],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3115486,"threshold_uncertainty_score":0.9819924,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07901769813769703,"score_gpt":0.4182809581552597,"score_spread":0.3392632600175627,"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."}}