{"id":"W3196218081","doi":"10.3390/jcm10173824","title":"Predicting Duration of Mechanical Ventilation in Acute Respiratory Distress Syndrome Using Supervised Machine Learning","year":2021,"lang":"en","type":"article","venue":"Journal of Clinical Medicine","topic":"Respiratory Support and Mechanisms","field":"Medicine","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Michael's Hospital","funders":"","keywords":"Medicine; ARDS; Mechanical ventilation; Acute respiratory distress; Gradient boosting; Intensive care unit; Random forest; Machine learning; Internal medicine; Lung","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":[0.001962281,0.0007111805,0.0007758648,0.001625381,0.0001989929,0.000519241,0.0006582692,0.0006146825,0.0008372958],"category_scores_gemma":[0.00535058,0.0001768939,0.0009380387,0.0006147423,0.0001888378,0.0004118269,0.0005413892,0.0007552174,0.0004153681],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003631521,"about_ca_system_score_gemma":0.000715835,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002791654,"about_ca_topic_score_gemma":0.003235562,"domain_scores_codex":[0.9990509,0.0003498155,0.0001237528,0.0002670092,0.0001149933,0.00009349479],"domain_scores_gemma":[0.9961013,0.002168949,0.0007591051,0.0002017985,0.0004960368,0.0002729058],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00103576,0.001009101,0.684294,0.0004449597,0.000745602,0.0003787079,0.0001310316,0.1578469,0.002486622,0.0003178075,0.007710265,0.1435993],"study_design_scores_gemma":[0.00004863523,0.0003636913,0.09251542,0.00008350734,0.0001097759,0.0002398851,0.00007907177,0.9024788,0.001415338,0.001531354,0.001100986,0.00003357471],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9127661,0.002099862,0.07430323,0.0006561387,0.0001748246,0.0001825211,0.007207976,0.001190556,0.001418703],"genre_scores_gemma":[0.9766773,0.0001962452,0.01551168,0.0001011192,0.0001017175,0.0000920469,0.007061406,0.00002557857,0.0002328064],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002791654,"threshold_uncertainty_score":0.01037765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.100389090260858,"score_gpt":0.4077795862555015,"score_spread":0.3073904959946435,"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."}}