{"id":"W2802312534","doi":"10.1017/cem.2018.292","title":"P094: A computerized provider order entry strategy to improve the quality of clinical information on neuroimaging requisitions from the emergency department: an interim analysis","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Emergency Medicine","topic":"Radiology practices and education","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Medicine; Emergency department; Neuroimaging; Context (archaeology); Interim analysis; Medical physics; Medical emergency; Emergency medicine; Surgery; Nursing; Randomized controlled trial; Psychiatry","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":[0.03274895,0.001493897,0.001468682,0.001356338,0.001049298,0.004825958,0.004242842,0.002739133,0.02412131],"category_scores_gemma":[0.0621409,0.0007944116,0.005836435,0.001914307,0.001518622,0.00476145,0.003061412,0.00394258,0.003133608],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007169268,"about_ca_system_score_gemma":0.03009115,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02311935,"about_ca_topic_score_gemma":0.01700281,"domain_scores_codex":[0.9784025,0.01328304,0.001635876,0.001174938,0.003345552,0.002158072],"domain_scores_gemma":[0.9365965,0.0357807,0.007181866,0.005154644,0.009410821,0.005875498],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.302214,0.06780206,0.1714869,0.003041473,0.00789635,0.0003927539,0.0008090463,0.007929993,0.002142635,0.004295067,0.04563903,0.3863507],"study_design_scores_gemma":[0.1715485,0.2782012,0.4364713,0.001656271,0.02114564,0.0005988244,0.001872501,0.03283509,0.0105915,0.004105242,0.04060013,0.0003739516],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9209492,0.00125657,0.008391048,0.01731109,0.001154633,0.01323419,0.02061737,0.001121062,0.01596479],"genre_scores_gemma":[0.9253809,0.000840396,0.03657098,0.008908038,0.0008417331,0.00840106,0.01220746,0.000264828,0.006584644],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03274895,"threshold_uncertainty_score":0.1731951,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2069300097311662,"score_gpt":0.4853872025706529,"score_spread":0.2784571928394868,"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."}}