{"id":"W2524111107","doi":"10.2196/humanfactors.6427","title":"Role of Large Clinical Datasets From Physiologic Monitors in Improving the Safety of Clinical Alarm Systems and Methodological Considerations: A Case From Philips Monitors","year":2016,"lang":"en","type":"article","venue":"JMIR Human Factors","topic":"Healthcare Technology and Patient Monitoring","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"ALARM; Computer science; Risk analysis (engineering); Data science; Reliability engineering; Medicine; Engineering","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":[0.002434411,0.0002400446,0.001144318,0.0001296374,0.0001723245,0.000008747999,0.0001675206,0.0006970379,0.00005094485],"category_scores_gemma":[0.003731539,0.0001283279,0.0001724672,0.0001262238,0.0005906635,0.000109389,0.000211936,0.0007858102,0.000003015846],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004319202,"about_ca_system_score_gemma":0.00009622671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002335698,"about_ca_topic_score_gemma":0.0001818593,"domain_scores_codex":[0.9951426,0.001772991,0.001954177,0.000567161,0.0002418367,0.0003211895],"domain_scores_gemma":[0.9890327,0.009264668,0.0006942502,0.0007347959,0.0001013424,0.0001722799],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002248212,0.0003474808,0.9841686,0.00004138843,0.0001138059,0.0001015543,0.000450855,4.845515e-7,0.008890826,0.0001704027,0.00005863511,0.005431104],"study_design_scores_gemma":[0.002270179,0.0007785201,0.9892233,0.0003607952,0.00009170362,0.0000190187,0.003097245,0.00004881391,0.002857238,0.0008999806,0.0001973882,0.0001558325],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9962887,0.0006342346,0.00002859491,0.0001475045,0.0005397706,0.0009028427,0.001382377,0.00006703904,0.000008933142],"genre_scores_gemma":[0.9988246,0.0001308863,0.0003944743,0.00003765728,0.000408528,0.00004438058,0.0001397792,0.00001644875,0.000003262613],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007491676,"threshold_uncertainty_score":0.5376195,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2666456462946552,"score_gpt":0.4898915770106107,"score_spread":0.2232459307159555,"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."}}