{"id":"W4409746284","doi":"10.1002/asmb.70012","title":"Assessing Latent Risk Based on Joint Modelling of Multiple Health Insurance Outcomes of Mixed Types","year":2025,"lang":"en","type":"article","venue":"Applied Stochastic Models in Business and Industry","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Actuarial science; Relevance (law); Health insurance; Disease; Latent class model; Health care; Econometrics; Medicine; Computer science; Business; Economics; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":[0.01505334,0.0007660369,0.001451415,0.001633952,0.0005625496,0.002452624,0.001447336,0.001265061,0.001810791],"category_scores_gemma":[0.04702066,0.0007649864,0.00188504,0.001145846,0.001607791,0.001806287,0.002049784,0.001598835,0.0001732464],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001642554,"about_ca_system_score_gemma":0.001451501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01079746,"about_ca_topic_score_gemma":0.008464688,"domain_scores_codex":[0.9937049,0.004534425,0.0002214817,0.0007593479,0.0004046865,0.000375247],"domain_scores_gemma":[0.9447963,0.047581,0.00418248,0.00175813,0.0009130567,0.0007691026],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006041398,0.000386718,0.1447299,0.00009944621,0.0007155526,0.0004189989,0.001072797,0.7302697,0.001161689,0.09605041,0.000460271,0.02403046],"study_design_scores_gemma":[0.00001588361,0.00006854483,0.005628756,0.00001790689,0.00005602811,0.00003710624,0.0001185371,0.9664704,0.0002142241,0.02719053,0.0001593578,0.0000227809],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.488234,0.0001764968,0.5098305,0.0005821475,0.00002471094,0.0001047363,0.0002357193,0.00008490511,0.0007267176],"genre_scores_gemma":[0.9659588,0.00008203724,0.03266863,0.00004115002,0.00002364341,0.0001431721,0.0001651616,0.0000148333,0.0009026765],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01505334,"threshold_uncertainty_score":0.07961059,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1675317494215937,"score_gpt":0.3524963648922756,"score_spread":0.1849646154706819,"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."}}