{"id":"W4225166168","doi":"10.1101/2022.04.24.22274125","title":"Towards Equitable Patient Subgroup Performance by Gene-Expression-Based Diagnostic Classifiers of Acute Infection","year":2022,"lang":"en","type":"preprint","venue":"medRxiv","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Infection and Immunity","funders":"Imperial College London; National Institute of Arthritis and Musculoskeletal and Skin Diseases; Università ta' Malta; Universitetet i Oslo; Sidra Medicine; National Institutes of Health; Cincinnati Children's Hospital Medical Center","keywords":"Machine learning; Artificial intelligence; Confounding; Classifier (UML); Leverage (statistics); Procalcitonin; Medicine; Computer science; Bioinformatics; Biology; Internal medicine; Sepsis","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000642219,0.0004894969,0.0008642651,0.0004210407,0.0002102731,0.00003457724,0.0003342457,0.0003690453,0.0009748586],"category_scores_gemma":[0.0009731303,0.0004725799,0.0003646407,0.0004346477,0.0001505625,0.00007930244,0.0009938132,0.001176512,0.00001898188],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009360304,"about_ca_system_score_gemma":0.001068374,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006182311,"about_ca_topic_score_gemma":0.000005594117,"domain_scores_codex":[0.9963905,0.000212143,0.0007971648,0.0008923828,0.001120945,0.0005868715],"domain_scores_gemma":[0.9969228,0.0007043059,0.0006278455,0.001253302,0.0002299135,0.000261841],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004400375,0.001651768,0.8595856,0.003388843,0.0004736922,0.0001246186,0.0006337059,0.0143524,0.05891733,0.000005466359,0.05419929,0.006227187],"study_design_scores_gemma":[0.00295827,0.002294384,0.24547,0.003199372,0.001874112,0.00001768351,0.00004410574,0.0115004,0.648739,0.00005444173,0.08270731,0.001140963],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9912023,0.0006240799,0.001246626,0.003011599,0.001964113,0.001199324,0.0003091682,0.0002128068,0.0002299645],"genre_scores_gemma":[0.9927657,0.001133586,0.000781932,0.00313085,0.0001366468,0.0009370195,0.0008385332,0.0001077778,0.0001680041],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6141157,"threshold_uncertainty_score":0.9999384,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0252927728636511,"score_gpt":0.2993615025637518,"score_spread":0.2740687297001007,"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."}}