{"id":"W2107398538","doi":"10.1542/peds.2011-0096","title":"A Decision-Tree Approach to Cost Comparison of Newborn Screening Strategies for Cystic Fibrosis","year":2012,"lang":"en","type":"article","venue":"PEDIATRICS","topic":"Cystic Fibrosis Research Advances","field":"Medicine","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Population and Public Health","funders":"National Center for Research Resources; Centers for Disease Control and Prevention; National Institutes of Health; National Institute of Diabetes and Digestive and Kidney Diseases; Georgia Clinical and Translational Science Alliance","keywords":"Medicine; Medical diagnosis; Newborn screening; Cutoff; Decision tree; Cystic fibrosis; Pediatrics; Machine learning; Computer science; Pathology; Internal medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02053666,0.001661808,0.002079496,0.003864358,0.0006329155,0.001554302,0.001752109,0.00198798,0.006606847],"category_scores_gemma":[0.05335736,0.0008094045,0.002812567,0.001931143,0.0008204553,0.001674591,0.001176104,0.001989782,0.000271264],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005171309,"about_ca_system_score_gemma":0.003576963,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008708873,"about_ca_topic_score_gemma":0.005064052,"domain_scores_codex":[0.9808816,0.01733765,0.0003380728,0.0004758894,0.0006002882,0.0003665148],"domain_scores_gemma":[0.9136621,0.08197875,0.001568087,0.0005342747,0.001538239,0.0007184805],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001737633,0.0003239997,0.003658246,0.0002570939,0.0005501398,0.00006629532,0.00009323878,0.960013,0.000117084,0.01006986,0.000771236,0.02234216],"study_design_scores_gemma":[0.0002718489,0.0008147101,0.00116297,0.00007771423,0.0001996182,0.00003590906,0.00005803721,0.981096,0.00009672302,0.01558322,0.0005811539,0.00002221898],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4812449,0.004571851,0.4927199,0.003024917,0.0003637739,0.004187417,0.003364229,0.0004978676,0.01002509],"genre_scores_gemma":[0.870251,0.001053906,0.1236609,0.0003947026,0.00007867896,0.002136896,0.001178064,0.0000456769,0.001200198],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02053666,"threshold_uncertainty_score":0.1086096,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.058816328195286,"score_gpt":0.3775580267633887,"score_spread":0.3187416985681027,"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."}}