{"id":"W2097070075","doi":"10.1109/titb.2010.2040394","title":"A Cusum-Based Multilevel Alerting Method for Physiological Monitoring","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Healthcare Technology and Patient Monitoring","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"CUSUM; Computer science; Statistics; Heuristics; Vital signs; Data mining; Medicine; Mathematics; Anesthesia","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000482426,0.000208616,0.0003774826,0.002005329,0.0001984878,0.000006110714,0.0001631242,0.0008720243,0.00002754088],"category_scores_gemma":[0.0001997225,0.0001772951,0.00008458801,0.0009065586,0.0002237576,0.0001930722,0.000002107451,0.001331356,0.00002952603],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001055992,"about_ca_system_score_gemma":0.0001028384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003268238,"about_ca_topic_score_gemma":0.000005403522,"domain_scores_codex":[0.9984639,0.0000196048,0.0006968537,0.0002248539,0.0001892694,0.0004054778],"domain_scores_gemma":[0.998927,0.0002258504,0.0001740806,0.0003818762,0.000194282,0.00009688766],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004671328,0.0003741103,0.004851269,0.0003138769,0.00004901611,0.000007738427,0.0003037786,0.0003687937,0.1859613,0.0003467534,0.00007110554,0.8068852],"study_design_scores_gemma":[0.008730841,0.00250083,0.007108957,0.0006808044,0.00008594333,0.00009983579,0.001074694,0.04827273,0.9268031,0.0008450621,0.003393257,0.0004039752],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3769979,0.000009174227,0.6142915,0.005905389,0.001302745,0.000816289,0.00001930227,0.0006225223,0.00003518813],"genre_scores_gemma":[0.8801855,0.00001186441,0.1187784,0.0003641166,0.00008636246,0.0005271347,0.0000182469,0.00001282913,0.0000154834],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8064812,"threshold_uncertainty_score":0.7229885,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03966927095291953,"score_gpt":0.3659587799928726,"score_spread":0.3262895090399531,"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."}}