{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1042243,0.001450419,0.00153848,0.001743314,0.001355054,0.002691081,0.003712448,0.002688124,0.003878958],"category_scores_gemma":[0.2713062,0.001437973,0.002955912,0.00275786,0.001873255,0.002639286,0.004602097,0.004252688,0.0005107589],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0026037,"about_ca_system_score_gemma":0.005487018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01968035,"about_ca_topic_score_gemma":0.02244586,"domain_scores_codex":[0.9431846,0.05197253,0.001021247,0.001473934,0.001866491,0.0004812744],"domain_scores_gemma":[0.6759901,0.3048734,0.006326721,0.007915354,0.004184324,0.0007100222],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0008171169,0.0007592521,0.1156326,0.001355812,0.002700808,0.005566045,0.01352808,0.3874571,0.001706884,0.1740559,0.009839349,0.286581],"study_design_scores_gemma":[0.0001306812,0.0003720192,0.00715012,0.0003475971,0.0003609059,0.0009177367,0.001284441,0.8988006,0.001259823,0.08256707,0.006675375,0.0001337498],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02942614,0.0002301177,0.9673996,0.000778098,0.00003687098,0.0005170351,0.0001637558,0.0002945148,0.001153852],"genre_scores_gemma":[0.206479,0.000250938,0.7898957,0.0002158978,0.00003852273,0.00173976,0.0002592024,0.0001812533,0.0009396735],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1042243,"threshold_uncertainty_score":0.5511975,"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."}}