{"id":"W2145681902","doi":"10.1159/000351025","title":"Personalized Approach to Growth Hormone Treatment: Clinical Use of Growth Prediction Models","year":2013,"lang":"en","type":"article","venue":"Hormone Research in Paediatrics","topic":"Growth Hormone and Insulin-like Growth Factors","field":"Medicine","cited_by":93,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Centre Hospitalier Universitaire Sainte-Justine","funders":"Pfizer; Eli Lilly and Company","keywords":"Growth hormone; Growth hormone treatment; Dosing; Medicine; Regimen; Variance (accounting); Predictive modelling; Internal medicine; Hormone; Statistics; Endocrinology; Mathematics","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":[0.005413589,0.0009845227,0.001213299,0.001165783,0.0002817575,0.001997688,0.001128512,0.0009591033,0.003084252],"category_scores_gemma":[0.02225727,0.0004377018,0.0008035976,0.001182283,0.0003759253,0.00115109,0.001005495,0.00187331,0.0009598635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009181866,"about_ca_system_score_gemma":0.001312596,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00502627,"about_ca_topic_score_gemma":0.003952622,"domain_scores_codex":[0.9972805,0.001817706,0.0001489472,0.0003140343,0.0003764677,0.00006227432],"domain_scores_gemma":[0.9924787,0.005812632,0.0004714257,0.0004390273,0.0006248149,0.0001733222],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0006936904,0.0004783778,0.0416997,0.0004818949,0.000743119,0.0004423546,0.0002590342,0.2409933,0.001175708,0.009561071,0.01481419,0.6886576],"study_design_scores_gemma":[0.0001766129,0.0003251287,0.008526427,0.0002319787,0.0002404936,0.0004867458,0.0001261222,0.9460031,0.001277384,0.02950871,0.01300592,0.00009148457],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04427438,0.005586177,0.9265199,0.00711276,0.0003065979,0.0004170695,0.0009230096,0.003043259,0.01181694],"genre_scores_gemma":[0.5545515,0.005753678,0.4323226,0.001360078,0.0004637073,0.000537123,0.001393903,0.0004468203,0.003170587],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005413589,"threshold_uncertainty_score":0.02863014,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2286726329041172,"score_gpt":0.3729908778281894,"score_spread":0.1443182449240722,"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."}}