{"id":"W2901668457","doi":"10.2196/10245","title":"Improving Provider Adoption With Adaptive Clinical Decision Support Surveillance: An Observational Study","year":2018,"lang":"en","type":"article","venue":"JMIR Human Factors","topic":"Venous Thromboembolism Diagnosis and Management","field":"Medicine","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Heart, Lung, and Blood Institute; Agency for Healthcare Research and Quality","keywords":"Medicine; Dashboard; Observational study; Clinical decision support system; Pulmonary embolism; Medical emergency; Emergency medicine; Medical physics; Radiology; Decision support system; Surgery; Computer science; Data mining; Internal medicine; Data science","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":[],"consensus_categories":[],"category_scores_codex":[0.0006865249,0.000232311,0.0004951844,0.0001338771,0.0002744024,0.00006779048,0.0001471088,0.0001004782,0.0005860705],"category_scores_gemma":[0.00007084112,0.0001653661,0.00008677926,0.0001868204,0.0001475357,0.0003119088,0.00009061916,0.0002118446,0.00005455424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001097588,"about_ca_system_score_gemma":0.0001338444,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000202937,"about_ca_topic_score_gemma":0.001066995,"domain_scores_codex":[0.9978241,0.00009967729,0.0005290008,0.0005783085,0.0006652229,0.0003036833],"domain_scores_gemma":[0.9985641,0.00009389614,0.0002264056,0.0005020761,0.000396199,0.0002173472],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003210925,0.003188922,0.9831612,0.00002034603,0.0001321114,0.00002147723,0.001442663,0.000002130098,0.00009878272,0.0002601204,0.002787159,0.008563964],"study_design_scores_gemma":[0.002136511,0.01475839,0.9790021,0.00003945149,0.00008330643,0.000002125318,0.002211043,0.0000832049,0.00003157364,0.0000432198,0.001401956,0.0002071534],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964046,0.000003720966,0.0004881773,0.00004583796,0.0002483843,0.001955849,0.000007805276,0.0001357263,0.0007099244],"genre_scores_gemma":[0.9975365,0.000002159491,0.0009718237,0.0003023595,0.0006354426,0.0001130344,0.0001238507,0.00004155812,0.0002732786],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01156947,"threshold_uncertainty_score":0.6743433,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1646674625975998,"score_gpt":0.4174821515031289,"score_spread":0.2528146889055292,"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."}}