{"id":"W2468369477","doi":"","title":"Strategies for handling normality assumptions in multi-level modeling: a case study estimating trajectories of Health Utilities Index Mark 3 scores.","year":2011,"lang":"en","type":"article","venue":"PubMed","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Statistics Canada","funders":"","keywords":"Statistics; Normality; Health Utilities Index; Index (typography); Econometrics; Mathematics; Goodness of fit; Normal distribution; Variance (accounting); Population; Demography; Medicine; Computer science; Economics; Environmental health; Health related quality of life","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":[0.005191083,0.0001743819,0.0003932819,0.0002710196,0.0006432014,0.000104466,0.0002571402,0.00006973519,0.000005027613],"category_scores_gemma":[0.0002486431,0.000181458,0.0001084793,0.0004298614,0.0002804409,0.0005611226,0.00006196297,0.0001488281,2.486451e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001241239,"about_ca_system_score_gemma":0.0002417022,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1430135,"about_ca_topic_score_gemma":0.2001229,"domain_scores_codex":[0.9973617,0.0004462599,0.0007753589,0.0003393569,0.000393793,0.0006835793],"domain_scores_gemma":[0.9990276,0.0001344887,0.0002849165,0.0002565115,0.0001958372,0.0001006177],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004977997,0.0008242151,0.8541188,0.000269851,0.00008322232,0.00002480312,0.1116636,0.002694653,7.923255e-8,0.004387103,0.000008775532,0.02587508],"study_design_scores_gemma":[0.0007673878,0.00003362859,0.7123547,0.00002435494,0.00002517613,0.000001434318,0.2617558,0.02247122,8.46205e-7,0.002383616,0.000008168004,0.0001736129],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9422696,0.0001922361,0.05268141,0.00003496276,0.0004345937,0.003437709,0.00006067012,0.00008333003,0.0008054604],"genre_scores_gemma":[0.9897457,0.00001877257,0.007678289,0.0000194597,0.00005423279,0.002426921,0.000003572307,0.00001546693,0.00003758327],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1500922,"threshold_uncertainty_score":0.8626933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3450288216177719,"score_gpt":0.3784305409436999,"score_spread":0.03340171932592806,"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."}}