{"id":"W6889105448","doi":"10.25384/sage.23732941.v1","title":"sj-ipynb-2-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":"Figshare","topic":"","field":"","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":[],"consensus_categories":[],"category_scores_codex":[0.001369043,0.002481162,0.001587472,0.001532813,0.0007446031,0.002370229,0.003615187,0.002440793,0.2654322],"category_scores_gemma":[0.007343942,0.001383842,0.002231463,0.002289544,0.0004786156,0.001983378,0.002403317,0.002707546,0.2302773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001343005,"about_ca_system_score_gemma":0.002180353,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009292402,"about_ca_topic_score_gemma":0.02335259,"domain_scores_codex":[0.9992255,0.0001811198,0.00007067158,0.0002400943,0.0001920256,0.00009067923],"domain_scores_gemma":[0.9983782,0.0008575558,0.00007775049,0.000343835,0.0002235338,0.0001192439],"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":[0.00004012964,0.00001821417,0.0003612536,0.0005540088,0.00003479575,0.00001870485,0.00001140362,0.001353763,0.0001511667,0.0008211287,0.9918704,0.004765177],"study_design_scores_gemma":[0.0004240718,0.00002888753,0.002048872,0.0003940405,0.00004517642,0.0001157787,0.0000405052,0.01006867,0.001519401,0.01655282,0.968691,0.00007080175],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003493755,0.0002953151,0.004533104,0.0004376554,0.0001278614,0.00006112163,0.9707704,0.01904975,0.004375494],"genre_scores_gemma":[0.002229555,0.0002890802,0.01108562,0.0005163014,0.00003544265,0.0005837543,0.9767087,0.004495368,0.004056141],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2654322,"threshold_uncertainty_score":0.8879591,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08236247393611759,"score_gpt":0.3953778853141648,"score_spread":0.3130154113780472,"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."}}