{"id":"W4200173399","doi":"10.3389/fped.2021.759776","title":"Comparison of Multivariable Logistic Regression and Machine Learning Models for Predicting Bronchopulmonary Dysplasia or Death in Very Preterm Infants","year":2021,"lang":"en","type":"article","venue":"Frontiers in Pediatrics","topic":"Neonatal Respiratory Health Research","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute; University of Toronto; University of British Columbia; Queen's University","funders":"Ontario Medical Association","keywords":"Bronchopulmonary dysplasia; Medicine; Logistic regression; Gestational age; Machine learning; Odds ratio; Area under the curve; Cohort; Pediatrics; Statistics; Artificial intelligence; Pregnancy; Internal medicine; Mathematics; Computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007410817,0.0001713007,0.0006213271,0.0004536139,0.00007853002,0.00001266243,0.0001021499,0.0002299156,0.000008346316],"category_scores_gemma":[0.004159178,0.0001498777,0.00003978589,0.0005664872,0.00005614905,0.0001630406,0.0001583671,0.0007471769,2.786336e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002526957,"about_ca_system_score_gemma":0.0006305295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001225266,"about_ca_topic_score_gemma":0.00003428435,"domain_scores_codex":[0.9978787,0.0001627069,0.0006620171,0.0004140876,0.0004255553,0.0004569207],"domain_scores_gemma":[0.9984822,0.0007580434,0.0002000019,0.0002228446,0.0001473628,0.0001894973],"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":[0.002831053,0.000184685,0.9676831,0.002874612,0.000009987741,0.0001888202,0.000361737,0.003756145,0.0001200514,0.00001067189,0.000232278,0.02174687],"study_design_scores_gemma":[0.005117788,0.0007304874,0.07581916,0.0006815305,0.00006541439,0.00002699372,0.00054905,0.9148714,0.000492276,0.0004030617,0.001046252,0.0001965671],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9411778,0.03691738,0.01986801,0.00006423072,0.0004743837,0.001124928,0.00006426169,0.00003449247,0.0002744707],"genre_scores_gemma":[0.9557447,0.003088664,0.04047754,0.0000400172,0.0001169911,0.00005860944,0.00007568424,0.00003822267,0.0003596049],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9111153,"threshold_uncertainty_score":0.6111837,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1111564947102509,"score_gpt":0.3894650578889659,"score_spread":0.278308563178715,"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."}}