{"id":"W2883325723","doi":"10.1097/ccm.0000000000003320","title":"Handovers Among Staff Intensivists: A Study of Information Loss and Clinical Accuracy to Anticipate Events*","year":2018,"lang":"en","type":"article","venue":"Critical Care Medicine","topic":"Hospital Admissions and Outcomes","field":"Medicine","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Sciences Centre; University of Toronto; Sunnybrook Health Science Centre","funders":"","keywords":"Medicine; Medical diagnosis; Morning; Emergency medicine; Anticipation (artificial intelligence); Observational study; Prospective cohort study; Patient safety; Medical emergency; Pediatrics; Internal medicine; Health care","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":[0.005340094,0.0002501226,0.0002609757,0.001073212,0.0003445847,0.001062953,0.0003534799,0.0005983037,0.0006675549],"category_scores_gemma":[0.05265157,0.0003719178,0.0003870311,0.000683633,0.0004258704,0.0007662607,0.0006293245,0.0004828983,0.0001406259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007361202,"about_ca_system_score_gemma":0.0006696233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00446967,"about_ca_topic_score_gemma":0.003114952,"domain_scores_codex":[0.9963894,0.001623765,0.0004653012,0.0003655429,0.0008746369,0.0002813015],"domain_scores_gemma":[0.9483879,0.02818313,0.01726657,0.001947803,0.00257788,0.001636744],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001395889,0.00004766843,0.9966917,0.00001496453,0.00004235256,0.00002324292,0.0005059079,0.00005019965,0.00005821795,0.00001121369,0.00003554267,0.002379376],"study_design_scores_gemma":[0.00001804495,0.000420709,0.9969891,0.00003072264,0.00002868531,0.0001028988,0.0008794391,0.001311933,0.00007867307,0.0000393519,0.00009297816,0.00000756211],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.999463,0.0001348353,0.0001465265,0.00003729139,0.000003693475,0.00001085764,0.00003934409,0.000002398847,0.0001620805],"genre_scores_gemma":[0.9997279,0.00004283952,0.0001261764,0.00001526832,0.00000565063,0.00000596904,0.00004239432,7.665419e-7,0.00003307787],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005340094,"threshold_uncertainty_score":0.02824146,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03467313774092845,"score_gpt":0.4166117737569086,"score_spread":0.3819386360159802,"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."}}