{"id":"W3016811362","doi":"10.2196/17648","title":"Toward Optimal Heparin Dosing by Comparing Multiple Machine Learning Methods: Retrospective Study","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Heparin-Induced Thrombocytopenia and Thrombosis","field":"Medicine","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Dosing; Computer science; Medicine; Heparin; Retrospective cohort study; Machine learning; Artificial intelligence; Medical physics; Intensive care medicine; Surgery; Internal medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009870746,0.0003937699,0.001281789,0.0001085575,0.0002388479,0.0001047208,0.0003705334,0.0002826566,0.0004953375],"category_scores_gemma":[0.001659595,0.0003324906,0.0001803168,0.0005629432,0.0001566064,0.000376191,0.0005035513,0.001948043,0.0001962717],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002237626,"about_ca_system_score_gemma":0.0001908078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001457477,"about_ca_topic_score_gemma":0.00001185107,"domain_scores_codex":[0.9964556,0.0002314083,0.00120933,0.0003167162,0.001174017,0.0006129549],"domain_scores_gemma":[0.9978416,0.0002153614,0.000300728,0.0003213098,0.0001333791,0.001187598],"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":[0.0006337569,0.001929595,0.8365954,0.000887855,0.001215053,0.0001707787,0.1216702,0.00009757635,0.004616448,0.0001563192,0.003635367,0.02839166],"study_design_scores_gemma":[0.01546355,0.004034545,0.07348167,0.000470595,0.0005378186,0.0001887989,0.03191589,0.8436938,0.006567042,0.00001911398,0.02249219,0.001134982],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.97289,0.0000739299,0.01817282,0.001383421,0.0001568183,0.001286045,0.000008151622,0.0004331052,0.005595772],"genre_scores_gemma":[0.9757173,0.00003880428,0.02207818,0.00170539,0.0001967948,0.00007002159,0.00009017032,0.00005033835,0.00005300044],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8435962,"threshold_uncertainty_score":0.9999127,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07641253536434223,"score_gpt":0.3733224485144085,"score_spread":0.2969099131500663,"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."}}