{"id":"W4281985242","doi":"10.1016/j.jclinepi.2022.05.008","title":"Part I: A friendly introduction to latent class analysis","year":2022,"lang":"en","type":"article","venue":"Journal of Clinical Epidemiology","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":69,"is_retracted":false,"has_abstract":false,"ca_institutions":"St. Michael's Hospital; Women's College Hospital; University of Toronto","funders":"","keywords":"Latent class model; Class (philosophy); Probabilistic logic; Set (abstract data type); Computer science; Population; Data science; Data mining; Risk analysis (engineering); Machine learning; Artificial intelligence; Medicine; Environmental health","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":[0.003024533,0.002332228,0.001954431,0.002212426,0.0007113804,0.003386816,0.002319943,0.003144469,0.1416064],"category_scores_gemma":[0.01522655,0.001527909,0.00202982,0.00243838,0.001267261,0.003488216,0.002253412,0.005449856,0.1106778],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008958171,"about_ca_system_score_gemma":0.001330276,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001076247,"about_ca_topic_score_gemma":0.001270177,"domain_scores_codex":[0.9974387,0.001188421,0.0001880323,0.0003037124,0.0008007718,0.00008040274],"domain_scores_gemma":[0.9942919,0.003718413,0.0001833305,0.0009471764,0.0007399615,0.0001191551],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008192764,0.0001525519,0.0003497415,0.001048362,0.00008536458,0.0002704236,0.0001662695,0.008501588,0.003949585,0.2131246,0.500333,0.2719364],"study_design_scores_gemma":[0.0000244586,0.00005145235,0.0008956526,0.0002850647,0.00002901571,0.0007626901,0.00004064956,0.03728309,0.00185465,0.3959566,0.5627092,0.0001074829],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002164002,0.003226744,0.9749404,0.001391187,0.002717827,0.0001459445,0.002387385,0.003683845,0.01129032],"genre_scores_gemma":[0.01176734,0.01013523,0.8472501,0.003733591,0.009429357,0.00184153,0.006514212,0.01173996,0.09758873],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1416064,"threshold_uncertainty_score":0.4737205,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1807855749113848,"score_gpt":0.4700970205305672,"score_spread":0.2893114456191824,"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."}}