{"id":"W4297225278","doi":"10.1287/opre.2022.2300","title":"Individualized Dynamic Patient Monitoring Under Alarm Fatigue","year":2022,"lang":"en","type":"article","venue":"Operations Research","topic":"Healthcare Technology and Patient Monitoring","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; University of Alberta; University of Calgary","funders":"","keywords":"ALARM; Computer science; Harm; Constant false alarm rate; False positive paradox; False alarm; Process (computing); Medical emergency; Medicine; Artificial intelligence; Psychology; Social psychology; Engineering","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":[0.002403762,0.000643578,0.0008303148,0.0004053504,0.000346955,0.001016917,0.001117466,0.001090787,0.001362917],"category_scores_gemma":[0.01653507,0.000413458,0.0004091326,0.0003811834,0.0007208927,0.001414665,0.001414649,0.001575231,0.0001765639],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001051452,"about_ca_system_score_gemma":0.001032899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002344593,"about_ca_topic_score_gemma":0.001645859,"domain_scores_codex":[0.9973889,0.001282904,0.00009169765,0.000531257,0.0004182469,0.0002870482],"domain_scores_gemma":[0.9916298,0.00572625,0.001154231,0.00070725,0.0004258936,0.0003565449],"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":[0.0005791874,0.0003369342,0.01013951,0.0001302103,0.0001154284,0.0002789441,0.0004104102,0.8140888,0.003631207,0.01739929,0.003246926,0.1496432],"study_design_scores_gemma":[0.00007689715,0.0003921856,0.003467537,0.00002625853,0.00004035492,0.0001769747,0.0001093407,0.9768927,0.001331402,0.01588345,0.001571715,0.00003114607],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2666769,0.0009463053,0.7211124,0.003567447,0.000176842,0.0001768746,0.0001434073,0.0007644563,0.006435382],"genre_scores_gemma":[0.9808134,0.0001518034,0.01821753,0.0001816889,0.00004564112,0.00003185434,0.00003289065,0.00001337103,0.0005119024],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002403762,"threshold_uncertainty_score":0.01271248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2306979716111738,"score_gpt":0.4962284166904971,"score_spread":0.2655304450793233,"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."}}