{"id":"W2996547294","doi":"10.2196/16912","title":"Accuracy and Effects of Clinical Decision Support Systems Integrated With BMJ Best Practice–Aided Diagnosis: Interrupted Time Series Study","year":2019,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Clinical decision support system; Medicine; Medical diagnosis; Logistic regression; Medical record; Interrupted Time Series Analysis; Observational study; Emergency medicine; Retrospective cohort study; Decision support system; Data mining; Internal medicine; Computer science; Radiology; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03680955,0.000628933,0.00120388,0.001432973,0.0004602954,0.001983479,0.001485827,0.001234563,0.003202009],"category_scores_gemma":[0.1771543,0.0005614999,0.003488896,0.002749641,0.0007212775,0.001690684,0.001898622,0.00259345,0.0005502003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001637241,"about_ca_system_score_gemma":0.001775189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01062864,"about_ca_topic_score_gemma":0.003021273,"domain_scores_codex":[0.9567403,0.02734446,0.004958587,0.004102803,0.005027596,0.001826413],"domain_scores_gemma":[0.7764645,0.144762,0.05051929,0.01682966,0.007526038,0.003898526],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01242832,0.001876853,0.9617807,0.0003723862,0.004067461,0.0001899245,0.001028737,0.003154095,0.0002321505,0.0003766762,0.0006318344,0.01386079],"study_design_scores_gemma":[0.0003433531,0.004162793,0.9704396,0.0001333675,0.001913947,0.0001581167,0.0006055196,0.02045288,0.0004187885,0.000368851,0.0009454426,0.00005739999],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9948637,0.000986298,0.001750355,0.0003294961,0.00007255879,0.0001550542,0.001277907,0.00002391193,0.0005407011],"genre_scores_gemma":[0.9973481,0.0002089422,0.0006448301,0.00007196738,0.00005288415,0.0001515267,0.001210146,0.00001206844,0.0002994537],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03680955,"threshold_uncertainty_score":0.1946698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04391165587405394,"score_gpt":0.4991384615141133,"score_spread":0.4552268056400594,"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."}}