{"id":"W4411018961","doi":"10.46254/an15.20250301","title":"When Less is More: Optimizing Prescription Alerts under Fatigue","year":2025,"lang":"en","type":"article","venue":"","topic":"Healthcare Technology and Patient Monitoring","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Medical prescription; Medicine; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001663387,0.000963702,0.000922417,0.0006769993,0.0004875564,0.001963932,0.0007175899,0.0009351251,0.001152596],"category_scores_gemma":[0.01681326,0.0003560011,0.0003544179,0.0003404311,0.0005003471,0.002176687,0.001006703,0.0007978015,0.0002609956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007966054,"about_ca_system_score_gemma":0.00242207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00518032,"about_ca_topic_score_gemma":0.004228962,"domain_scores_codex":[0.9987802,0.0005056792,0.00007484498,0.0002720198,0.0001953887,0.0001718927],"domain_scores_gemma":[0.9963688,0.002155705,0.0005244326,0.0001359321,0.0005994402,0.000215725],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001531445,0.001264509,0.02701612,0.0005348845,0.0002832962,0.0003153735,0.001092231,0.635466,0.02641076,0.006810844,0.004089586,0.295185],"study_design_scores_gemma":[0.00008336492,0.0006636987,0.00607202,0.00006866429,0.0001271012,0.00009485264,0.0003877609,0.9744824,0.005587267,0.01092505,0.001444119,0.00006363152],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.566055,0.001499832,0.4204437,0.003922608,0.0001745412,0.0003118299,0.0002087887,0.000919938,0.00646373],"genre_scores_gemma":[0.9783385,0.0001755019,0.02037473,0.000359921,0.00003039037,0.00004271974,0.00004394628,0.00003424407,0.0005998907],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00518032,"threshold_uncertainty_score":0.01030034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09534501760019048,"score_gpt":0.3676206038582756,"score_spread":0.2722755862580851,"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."}}