{"id":"W4313596624","doi":"10.1016/j.ibmed.2023.100087","title":"Discriminating Acute Respiratory Distress Syndrome from other forms of respiratory failure via iterative machine learning","year":2023,"lang":"en","type":"article","venue":"Intelligence-Based Medicine","topic":"Respiratory Support and Mechanisms","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Toronto","keywords":"ARDS; Medicine; Mechanical ventilation; Intensive care medicine; Acute respiratory distress; Respiratory failure; Demographics; Emergency medicine; Lung; Internal medicine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001080378,0.0005553947,0.001145906,0.0007724112,0.0002029555,0.00001969985,0.0003683806,0.000296827,0.001881972],"category_scores_gemma":[0.0006452043,0.0004002709,0.0002656876,0.001157574,0.0005117828,0.000200888,0.0001059296,0.0008306839,0.000182036],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001486688,"about_ca_system_score_gemma":0.000266005,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002066975,"about_ca_topic_score_gemma":0.0001138939,"domain_scores_codex":[0.9959673,0.0002037162,0.001284271,0.0007663324,0.00109532,0.000683045],"domain_scores_gemma":[0.9972863,0.0005513721,0.0005670399,0.0007714755,0.0003556472,0.0004682102],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002248977,0.0006397988,0.1145318,0.001424314,0.001421532,0.006684579,0.008801039,0.0005802143,0.7048604,0.002214249,0.003121958,0.1534711],"study_design_scores_gemma":[0.006425686,0.01425993,0.01103228,0.007082347,0.001628199,0.0001212909,0.009890275,0.01057142,0.830318,0.004473346,0.1025789,0.001618255],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9333457,0.001276918,0.05923427,0.001309688,0.0006666776,0.001216385,0.0003080695,0.0005539858,0.002088332],"genre_scores_gemma":[0.9951013,0.00002529648,0.0005605869,0.002355813,0.0003676367,0.0001085096,0.0004651396,0.0001276189,0.0008880385],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1518528,"threshold_uncertainty_score":0.9998449,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04599419116801513,"score_gpt":0.3184280605876282,"score_spread":0.2724338694196131,"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."}}