{"id":"W4401514159","doi":"10.1097/ccm.0000000000006390","title":"Machine Learning Tools for Acute Respiratory Distress Syndrome Detection and Prediction","year":2024,"lang":"en","type":"review","venue":"Critical Care Medicine","topic":"Respiratory Support and Mechanisms","field":"Medicine","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Medical Research Council","keywords":"ARDS; Medicine; Intensive care medicine; Acute respiratory distress; Pneumonia; Sepsis; Psychological intervention; Systemic inflammatory response syndrome; Machine learning; Artificial intelligence; Lung; Computer science; Surgery; Internal medicine","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":[0.001736378,0.001234711,0.001616183,0.003083277,0.0002066186,0.001263894,0.001291512,0.001568724,0.003553472],"category_scores_gemma":[0.00479852,0.0003573871,0.001509354,0.002227522,0.0005400244,0.001441208,0.000864098,0.003119049,0.002354548],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007502968,"about_ca_system_score_gemma":0.001223286,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001176271,"about_ca_topic_score_gemma":0.001178835,"domain_scores_codex":[0.999194,0.0002369936,0.0001122822,0.0001295372,0.0002811224,0.0000461112],"domain_scores_gemma":[0.997426,0.001898261,0.0001771307,0.00005399904,0.0003841656,0.00006039904],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003896352,0.00006693236,0.0004291769,0.01149627,0.0003047183,0.00007475273,0.00003529258,0.00119247,0.000494396,0.003934304,0.02163608,0.9602965],"study_design_scores_gemma":[0.00004726628,0.0002753123,0.003037062,0.01948306,0.0007906932,0.001352543,0.00008387297,0.003703587,0.001472897,0.01768653,0.9519404,0.0001266972],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0001716293,0.9948615,0.002371816,0.0008513388,0.0004364337,0.0000194009,0.00007743164,0.00005102981,0.00115929],"genre_scores_gemma":[0.002347713,0.9928107,0.002949935,0.0005696309,0.0005702953,0.00003189308,0.0001566842,0.00001001489,0.0005531445],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.003553472,"threshold_uncertainty_score":0.01188755,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07322389844538427,"score_gpt":0.3969804319466749,"score_spread":0.3237565335012906,"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."}}