{"id":"W2098381794","doi":"10.1109/iembs.2006.260771","title":"Predicting Probability of Mortality in the Neonatal Intensive Care Unit","year":2006,"lang":"en","type":"article","venue":"","topic":"Sepsis Diagnosis and Treatment","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; University of Ottawa","funders":"","keywords":"Artificial neural network; Receiver operating characteristic; Neonatal intensive care unit; Computer science; Intensive care unit; Intensive care; A priori and a posteriori; Machine learning; Artificial intelligence; Statistics; Medicine; Intensive care medicine; Pediatrics; Mathematics","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":[0.001140695,0.0002254552,0.0002143079,0.0004436205,0.00009633454,0.0002647495,0.000206096,0.0002673181,0.0004920794],"category_scores_gemma":[0.01238197,0.0001059397,0.0001135096,0.000233001,0.0001392031,0.0003155407,0.0002784257,0.0003811353,0.0001215812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003056469,"about_ca_system_score_gemma":0.0003057258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002574566,"about_ca_topic_score_gemma":0.002164299,"domain_scores_codex":[0.999673,0.0001990735,0.00002206259,0.00002829598,0.00005825198,0.0000193036],"domain_scores_gemma":[0.9968901,0.00247869,0.0002520068,0.00007153195,0.0002392268,0.00006840419],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007821143,0.0001536559,0.6503362,0.00007098717,0.00009767468,0.0002829795,0.0001158229,0.2307474,0.00170137,0.0008317108,0.001487569,0.1133925],"study_design_scores_gemma":[0.00001739688,0.0002977175,0.1488535,0.00002275833,0.00002178336,0.0001455309,0.00007659907,0.8461508,0.002371881,0.001733235,0.0002907233,0.00001807311],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9675964,0.0002541005,0.03087734,0.0002291621,0.00001773965,0.00002185901,0.0001621371,0.0001081733,0.0007330194],"genre_scores_gemma":[0.9945554,0.0001124941,0.00496851,0.0000113203,0.00001126487,0.00001958948,0.0001507891,0.000004053072,0.0001665817],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002574566,"threshold_uncertainty_score":0.006032646,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1055611708318039,"score_gpt":0.3510612708067806,"score_spread":0.2455000999749767,"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."}}