{"id":"W4384133705","doi":"10.1016/j.ijmedinf.2023.105143","title":"A novel systematic pipeline for increased predictability and explainability of growth patterns in children using trajectory features","year":2023,"lang":"en","type":"article","venue":"International Journal of Medical Informatics","topic":"Health, Environment, Cognitive Aging","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"St. Michael's Hospital; Institute for Clinical Evaluative Sciences; University of Toronto; SickKids Foundation; Centre for Global Health Research; Hospital for Sick Children; Public Health Ontario","funders":"Wellcome Trust","keywords":"Predictability; Artificial intelligence; Machine learning; Pipeline (software); Computer science; Trajectory; Predictive value; Pattern recognition (psychology); Statistics; Mathematics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004413915,0.0001070591,0.0003077419,0.0001670515,0.00002915009,0.00001738783,0.0004004925,0.00009956209,0.00007220352],"category_scores_gemma":[0.003363899,0.00008601153,0.0000743935,0.0001305345,0.0001756957,0.0003602557,0.0001817642,0.0002761508,0.000001814486],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002904137,"about_ca_system_score_gemma":0.0000803482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003823113,"about_ca_topic_score_gemma":0.0000532031,"domain_scores_codex":[0.9969669,0.00009766244,0.001236085,0.00009364413,0.001428734,0.0001769853],"domain_scores_gemma":[0.9984264,0.0006315661,0.0005934081,0.0001022965,0.00006742466,0.0001789313],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001114261,0.000360555,0.9873173,0.003218952,0.0001015447,0.00001393291,0.004652472,0.001192433,0.0003536985,0.00005385207,0.0001224968,0.002501353],"study_design_scores_gemma":[0.002441704,0.00008090576,0.8708125,0.002195994,0.00004528961,0.0003663457,0.001858847,0.1213617,0.0003547256,0.0003501633,0.000005010078,0.0001267746],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9567437,0.00001630787,0.0422882,0.0003045526,0.0001147687,0.0004363268,0.00006369421,0.000006837809,0.00002560069],"genre_scores_gemma":[0.9973652,0.00008030493,0.002207061,0.00025479,0.00006411943,0.000009747529,0.00001009178,0.00000703487,0.000001606842],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1201693,"threshold_uncertainty_score":0.4027144,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01807230365974944,"score_gpt":0.2990050959053708,"score_spread":0.2809327922456214,"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."}}