{"id":"W4308867610","doi":"10.3389/ijph.2022.1605047","title":"Predicting Low Cognitive Ability at Age 5—Feature Selection Using Machine Learning Methods and Birth Cohort Data","year":2022,"lang":"en","type":"article","venue":"International Journal of Public Health","topic":"Birth, Development, and Health","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Children's Research Centre; Health Research Board; Health Service Executive; Wellcome Trust; Canadian Institute for Theoretical Astrophysics","keywords":"Feature selection; Cohort; Random forest; Artificial intelligence; Population; Machine learning; Medicine; Cohort study; Computer science; Environmental health","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008323273,0.0007165768,0.0004703198,0.001748794,0.0002893531,0.000637146,0.0006102133,0.0004628653,0.0007891899],"category_scores_gemma":[0.01094579,0.0001578794,0.001353708,0.0007113086,0.0002130691,0.0003437559,0.0004167324,0.0009423923,0.0002605198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004773405,"about_ca_system_score_gemma":0.0009082659,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01328794,"about_ca_topic_score_gemma":0.01096284,"domain_scores_codex":[0.9988952,0.000631462,0.00007434989,0.0002006335,0.0001170128,0.00008140034],"domain_scores_gemma":[0.9954104,0.003208151,0.0004541393,0.0003778394,0.0003846139,0.0001649226],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003433419,0.0001589213,0.951679,0.00003368893,0.0003728175,0.0001343668,0.00006159003,0.01436026,0.0005414935,0.000106392,0.0005745486,0.03163349],"study_design_scores_gemma":[0.00009224112,0.0008223942,0.7165588,0.000113557,0.0002830109,0.0003528835,0.0001587685,0.2777795,0.001786047,0.001025461,0.0009801601,0.00004708341],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9728391,0.0004600136,0.02448493,0.0002229298,0.00004157011,0.00006691856,0.001482368,0.0001376924,0.0002645343],"genre_scores_gemma":[0.9835885,0.0001275719,0.01406592,0.00003444074,0.00001848882,0.00005590331,0.001879277,0.00001022067,0.0002196731],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01328794,"threshold_uncertainty_score":0.04401815,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1076588238625391,"score_gpt":0.4422857899482101,"score_spread":0.3346269660856711,"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."}}