{"id":"W4315784349","doi":"10.1371/journal.pdig.0000179","title":"External validity of machine learning-based prognostic scores for cystic fibrosis: A retrospective study using the UK and Canadian registries","year":2023,"lang":"en","type":"article","venue":"PLOS Digital Health","topic":"Cystic Fibrosis Research Advances","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Toronto; Chung-Ang University; Cystic Fibrosis Canada; Cystic Fibrosis Trust; Cystic Fibrosis Foundation","keywords":"Cystic fibrosis; Referral; Medicine; External validity; Internal medicine; Machine learning; Intensive care medicine; Artificial intelligence; Computer science; Statistics; Family medicine; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004629612,0.0001537096,0.0003923702,0.0002101723,0.0003244473,0.00009800537,0.0001062888,0.00002917002,0.000006682624],"category_scores_gemma":[0.004693567,0.0001098237,0.00005637616,0.0004665816,0.000240907,0.0001046827,0.00005041884,0.0002245012,0.000002264127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000339279,"about_ca_system_score_gemma":0.00125021,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01876352,"about_ca_topic_score_gemma":0.02245127,"domain_scores_codex":[0.9982665,0.00009062969,0.0003215051,0.0003090617,0.0004964701,0.0005157994],"domain_scores_gemma":[0.9982001,0.0008261347,0.0001547082,0.0002334941,0.000212797,0.0003727282],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00057927,0.0001985976,0.9963247,0.0009530659,0.00005354732,0.00002348015,0.0005540278,0.0001293441,0.0001422209,0.00002900365,0.000124396,0.0008884054],"study_design_scores_gemma":[0.002016229,0.008370821,0.9543917,0.001298885,0.00007958895,0.00002654047,0.00132484,0.03135998,0.0002248757,0.0006185003,0.0001294463,0.0001585448],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9946655,0.0005775245,0.000523766,0.0008792765,0.0000364267,0.002554825,0.0006788748,0.00006384877,0.00001998299],"genre_scores_gemma":[0.9991242,0.0000149318,0.0004452801,0.00004617154,0.00006205865,0.00009991336,0.00008007731,0.00002938614,0.00009797455],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0419329,"threshold_uncertainty_score":0.9953864,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06083467966781247,"score_gpt":0.3472651567124134,"score_spread":0.286430477044601,"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."}}