{"id":"W2135033947","doi":"10.1109/titb.2002.1006305","title":"Extending ventilation duration estimations approach from adult to neonatal intensive care patients using artificial neural networks","year":2002,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Respiratory Support and Mechanisms","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; Children's Hospital of Eastern Ontario; Nortel (Canada); University of Ottawa","funders":"","keywords":"Mechanical ventilation; Overfitting; Neonatal intensive care unit; Medicine; Artificial ventilation; Intensive care unit; Intensive care; Artificial neural network; Ventilation (architecture); Duration (music); Intensive care medicine; Emergency medicine; Pediatrics; Computer science; Anesthesia; Machine learning; Internal medicine; Engineering; Respiratory disease","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":[0.00007064145,0.0002125393,0.000289068,0.001960873,0.0002037462,0.00002908178,0.00009938572,0.0003365357,0.0001096783],"category_scores_gemma":[0.00007667632,0.0002051867,0.00006569093,0.001368761,0.0000947624,0.0006295759,0.000003458911,0.0004193095,0.00005403],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002791022,"about_ca_system_score_gemma":0.00002164751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003637287,"about_ca_topic_score_gemma":0.0000112512,"domain_scores_codex":[0.9983627,0.00001905167,0.0007940091,0.0002287231,0.0003189296,0.0002765811],"domain_scores_gemma":[0.9986513,0.00002507498,0.0002053271,0.00028613,0.0007178381,0.0001143049],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008232496,0.0004037639,0.002959926,0.000134133,0.0001253823,0.00002457611,0.01298738,0.08032043,0.00616959,0.0006611241,0.0002928821,0.8950976],"study_design_scores_gemma":[0.002934129,0.0009924094,0.001243429,0.0002599694,0.0001540731,0.0000453477,0.01511784,0.9610808,0.0173928,0.000126312,0.000334471,0.0003184473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3949386,0.00002033023,0.6029633,0.0005547833,0.0006457018,0.0006517131,0.00003888428,0.0001482254,0.00003852081],"genre_scores_gemma":[0.9915615,0.000008534236,0.006807498,0.0009983254,0.00006540713,0.0001086091,0.0004244015,0.00001652557,0.00000924178],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8947791,"threshold_uncertainty_score":0.8367271,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02115592857264783,"score_gpt":0.2609623797273681,"score_spread":0.2398064511547202,"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."}}