{"id":"W6982293688","doi":"","title":"How many fit all? Latent class analysis of administrative data on healthcare utilization by persons with dementia in Quebec, Canada","year":2022,"lang":"en","type":"dissertation","venue":"Open MIND","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Dementia; Latent class model; Health care; Class (philosophy); Data collection; MEDLINE","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002877969,0.0002336806,0.0006510286,0.0001468574,0.00007023024,0.0001364448,0.000688822,0.0001031592,0.002506836],"category_scores_gemma":[0.0002667468,0.0001952033,0.00003523038,0.0005465916,0.00002174182,0.0001035789,0.00009112428,0.0002779734,4.6194e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002272515,"about_ca_system_score_gemma":0.001290215,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.53016,"about_ca_topic_score_gemma":0.9961606,"domain_scores_codex":[0.9980652,0.0002802667,0.0003961752,0.0005623209,0.0004970116,0.0001990063],"domain_scores_gemma":[0.9982135,0.0004869039,0.0005209254,0.0006007763,0.00009385851,0.00008403261],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.004456886,0.003638217,0.01760724,0.003751158,0.02577163,0.0008119758,0.03035292,0.00006557281,0.0002680439,0.09717672,0.1388497,0.6772499],"study_design_scores_gemma":[0.008975916,0.005961385,0.1774356,0.006523697,0.06363378,0.00001064367,0.3486535,0.07484345,0.007533113,0.01685643,0.2800629,0.009509549],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5760628,0.002340351,0.1123949,0.01999625,0.00172149,0.01756397,0.2247496,0.00002440457,0.04514627],"genre_scores_gemma":[0.6081534,0.0001057791,0.2547043,0.0002108675,0.00002896186,0.000297312,0.09868634,0.0001179352,0.03769508],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6677403,"threshold_uncertainty_score":0.998405,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2677525497237537,"score_gpt":0.4593954365268536,"score_spread":0.1916428868030998,"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."}}