{"id":"W6964162509","doi":"10.25384/sage.23732935","title":"sj-csv-4-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":"","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001832273,0.001973501,0.001372545,0.001492628,0.0005916943,0.002494967,0.003208404,0.001736695,0.3015453],"category_scores_gemma":[0.009406447,0.00126404,0.00187189,0.002400758,0.000488378,0.002229819,0.002311823,0.0029607,0.2620032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001256641,"about_ca_system_score_gemma":0.002310372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008673009,"about_ca_topic_score_gemma":0.01881509,"domain_scores_codex":[0.9989609,0.0002616472,0.0001118427,0.0002625996,0.0002909214,0.000112192],"domain_scores_gemma":[0.9977355,0.001271714,0.00008907932,0.0004473341,0.0003172904,0.0001390369],"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":[0.00002811531,0.00001654405,0.0002123623,0.0003470884,0.00002190239,0.00001081018,0.00001115464,0.001126567,0.0001069955,0.001000283,0.991875,0.005243184],"study_design_scores_gemma":[0.0004010305,0.00002442833,0.001526741,0.0003036708,0.0000279235,0.00008142177,0.00004587201,0.01532052,0.001432836,0.02184871,0.9589203,0.0000665659],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003895157,0.0002646396,0.01089401,0.0007151262,0.0001772148,0.0001062964,0.9292438,0.05093291,0.007276532],"genre_scores_gemma":[0.00319911,0.0003481565,0.0253678,0.0008535707,0.00006409905,0.0009352547,0.9492128,0.01361638,0.006402822],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3015453,"threshold_uncertainty_score":0.9962609,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1010311792790116,"score_gpt":0.4300257142863054,"score_spread":0.3289945350072939,"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."}}