{"id":"W2989963722","doi":"","title":"Measuring overuse with electronic health records data.","year":2018,"lang":"en","type":"article","venue":"PubMed","topic":"Healthcare cost, quality, practices","field":"Health Professions","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Thomas Hospital","funders":"","keywords":"Medicine; Logistic regression; Chart; Medical prescription; Electronic health record; Health records; Health care; Emergency medicine; Internal medicine; Statistics; Nursing","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01170096,0.0002269852,0.0004131127,0.0001386905,0.001269532,0.00003030444,0.0007510446,0.0001758204,0.0002027163],"category_scores_gemma":[0.002303676,0.0001891714,0.00002545862,0.0004944039,0.0001376046,0.0008940958,0.0004000498,0.001445108,0.0005989256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001124098,"about_ca_system_score_gemma":0.002759302,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.007577379,"about_ca_topic_score_gemma":0.03597251,"domain_scores_codex":[0.9925131,0.003001381,0.0007872358,0.0007530197,0.0006177818,0.002327547],"domain_scores_gemma":[0.9955786,0.0009020303,0.000695756,0.001823848,0.0003455022,0.000654192],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001050072,0.000275174,0.3528675,0.001236247,0.0002104283,0.00001193477,0.00436009,3.844678e-7,0.00000439411,0.005811609,0.08203743,0.5521348],"study_design_scores_gemma":[0.0008458932,0.0001801039,0.4375476,0.0001105709,0.00001958755,0.000006443042,0.0006574351,0.00002437623,0.00001172286,0.0004280109,0.5599303,0.0002380218],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7731057,0.002949641,0.002068675,0.1314433,0.004880635,0.01170987,0.0002510694,0.00149413,0.07209696],"genre_scores_gemma":[0.9672709,0.0004823664,0.0006907758,0.02432327,0.002331185,0.001883139,0.00007181495,0.00008837241,0.002858156],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5518967,"threshold_uncertainty_score":0.9990312,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7801379221822541,"score_gpt":0.528328963542618,"score_spread":0.251808958639636,"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."}}