{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006025943,0.0003540011,0.00063955,0.001673701,0.002798683,0.002446922,0.001486969,0.0005711967,0.002428033],"category_scores_gemma":[0.02156867,0.0002644642,0.0008502497,0.005375242,0.001180854,0.0006764518,0.0007940196,0.001334401,0.0002611582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03557998,"about_ca_system_score_gemma":0.04861577,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9978098,"about_ca_topic_score_gemma":0.9981204,"domain_scores_codex":[0.9976898,0.000925455,0.0001218254,0.0002764349,0.0005049073,0.0004815973],"domain_scores_gemma":[0.9910861,0.003916848,0.0007504736,0.000285679,0.003058077,0.0009028579],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002082689,0.0001446404,0.907822,0.00009891221,0.0002590266,0.00007943424,0.004622794,0.003721128,0.0001460706,0.003687471,0.03194216,0.04726813],"study_design_scores_gemma":[0.00003152625,0.00003035211,0.9585599,0.0002666597,0.0001232964,0.0000316993,0.01243223,0.02050391,0.0001318048,0.001853491,0.005978719,0.00005646723],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9594052,0.003683894,0.004617187,0.01282746,0.0001137971,0.0002126597,0.0136343,0.00006981784,0.005435672],"genre_scores_gemma":[0.9857266,0.001807343,0.003830435,0.0005467079,0.00002993028,0.00008860094,0.003929461,0.00003026917,0.004010634],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03557998,"threshold_uncertainty_score":0.258152,"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."}}