{"id":"W4393042834","doi":"10.3390/jcm13061811","title":"Predicting the Length of Mechanical Ventilation in Acute Respiratory Disease Syndrome Using Machine Learning: The PIONEER Study","year":2024,"lang":"en","type":"article","venue":"Journal of Clinical Medicine","topic":"Respiratory Support and Mechanisms","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Impact; St. Michael's Hospital","funders":"Fundación Canaria Instituto de Investigación Sanitaria de Canarias; Instituto de Salud Carlos III","keywords":"Medicine; ARDS; Mechanical ventilation; Cohort; Logistic regression; Internal medicine; Acute respiratory distress; 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":[0.007084421,0.0006917426,0.0005300819,0.0007241191,0.0003146542,0.000689106,0.0006737489,0.0005488278,0.0008752134],"category_scores_gemma":[0.01032057,0.0002444453,0.000978763,0.000382178,0.000531221,0.0008380038,0.000621423,0.001282224,0.0003133953],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004155946,"about_ca_system_score_gemma":0.0007354502,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005618304,"about_ca_topic_score_gemma":0.005755784,"domain_scores_codex":[0.9988452,0.0007209305,0.00004130536,0.0001889054,0.0001574839,0.00004610143],"domain_scores_gemma":[0.9907363,0.005546714,0.001249012,0.001209316,0.00071695,0.0005417757],"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":[0.001513116,0.0004616224,0.9685957,0.00009077349,0.0008141005,0.0002244777,0.000287249,0.002174249,0.001001192,0.0002860642,0.001070418,0.02348111],"study_design_scores_gemma":[0.0005242695,0.004074686,0.9618944,0.0001393772,0.0008628628,0.0009622038,0.0002255261,0.02623831,0.001389407,0.0008846768,0.002738385,0.00006583946],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9943124,0.001434755,0.002281182,0.0002560249,0.00002688395,0.00004340846,0.0004952221,0.0000331811,0.001116961],"genre_scores_gemma":[0.992496,0.001014329,0.004461919,0.0001371065,0.0001552481,0.00004821812,0.0008960498,0.00002054998,0.000770628],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007084421,"threshold_uncertainty_score":0.03746641,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1147748922030288,"score_gpt":0.4356078146164147,"score_spread":0.3208329224133859,"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."}}