{"id":"W6907938708","doi":"10.25384/sage.23732941","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":"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":[],"consensus_categories":[],"category_scores_codex":[0.001577461,0.001816028,0.001243231,0.001227547,0.0005705745,0.002023089,0.002967035,0.001623115,0.2202952],"category_scores_gemma":[0.00746474,0.001176324,0.001710498,0.001877709,0.0004345296,0.001754444,0.002165515,0.002616655,0.1863727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001093558,"about_ca_system_score_gemma":0.002135684,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007474894,"about_ca_topic_score_gemma":0.01881027,"domain_scores_codex":[0.9992062,0.000188795,0.00008266426,0.0002132221,0.0002198531,0.00008922671],"domain_scores_gemma":[0.998336,0.0008647509,0.00008350895,0.0003646933,0.0002310204,0.0001199938],"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":[0.0000429913,0.00002207783,0.0003738356,0.0004092018,0.00002969441,0.00001566763,0.0000119327,0.001209281,0.0001576455,0.0009612052,0.9914398,0.005326665],"study_design_scores_gemma":[0.0004790642,0.00002855192,0.002246876,0.0003208641,0.00003266154,0.00009798382,0.00004272187,0.0136943,0.001697115,0.01705785,0.9642391,0.00006291649],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0004981997,0.0002482955,0.007674931,0.0005829958,0.0001471867,0.00009186276,0.9532743,0.03215812,0.005324101],"genre_scores_gemma":[0.003079485,0.000279077,0.01976006,0.0006712595,0.0000449842,0.0008565046,0.9627734,0.007625575,0.004909673],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2202952,"threshold_uncertainty_score":0.7369608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1004958053367264,"score_gpt":0.4294654982364615,"score_spread":0.3289696928997351,"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."}}