{"id":"W4390751015","doi":"10.1038/s41390-023-02988-2","title":"Early prediction of pediatric asthma in the Canadian Healthy Infant Longitudinal Development (CHILD) birth cohort using machine learning","year":2024,"lang":"en","type":"article","venue":"Pediatric Research","topic":"Asthma and respiratory diseases","field":"Medicine","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; Canadian Institute for Advanced Research; University of Manitoba; University of Alberta; University of Toronto; BC Children's Hospital; University of British Columbia; Hospital for Sick Children","funders":"Canadian Institutes of Health Research; Genome Canada","keywords":"Asthma; Medicine; Atopy; Pediatrics; Receiver operating characteristic; Wheeze; Cohort; Early childhood; Internal medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00235248,0.0005217987,0.0003421528,0.001416658,0.0009198831,0.001079441,0.0009025502,0.0004007669,0.001137897],"category_scores_gemma":[0.0046623,0.0002826979,0.0006844439,0.001617243,0.0002924019,0.0003040307,0.0006701279,0.001067298,0.0002262434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005616525,"about_ca_system_score_gemma":0.009058718,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8784439,"about_ca_topic_score_gemma":0.8878925,"domain_scores_codex":[0.9992355,0.0001261794,0.00003728738,0.0001419688,0.0002737478,0.0001853327],"domain_scores_gemma":[0.9978582,0.0004056556,0.0004724732,0.0001848363,0.0007326658,0.0003460941],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005409574,0.00001405131,0.9948925,0.00001450638,0.00006781018,0.00003734776,0.00005868017,0.0003367539,0.00006048419,0.00005507353,0.0006381961,0.003770464],"study_design_scores_gemma":[0.000003995327,0.00001850192,0.9976287,0.00002113478,0.00003923478,0.00003546069,0.00006005266,0.00153631,0.00005658155,0.00004170606,0.0005526795,0.000005648706],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.985164,0.002418317,0.001265218,0.000699984,0.0000377731,0.00005903983,0.008279751,0.00006320105,0.002012623],"genre_scores_gemma":[0.993248,0.0008470874,0.001166157,0.00005267041,0.00001046931,0.00002348429,0.004173029,0.000008831302,0.0004703281],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1215561,"threshold_uncertainty_score":0.2445439,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06366117843332474,"score_gpt":0.3547626074581085,"score_spread":0.2911014290247838,"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."}}