{"id":"W2048205624","doi":"10.1191/0962280203sm323ra","title":"Latent mixed Markov modelling of smoking transitions using Monte Carlo bootstrapping","year":2003,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","topic":"Smoking Behavior and Cessation","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Health Information; Western University","funders":"","keywords":"Bootstrapping (finance); Markov chain; Markov chain Monte Carlo; Statistics; Goodness of fit; Econometrics; Markov model; Mathematics; Computer science; Bayesian probability; Psychology","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":[0.009715836,0.0008014609,0.001702022,0.001570685,0.000900868,0.001547419,0.002756571,0.001717385,0.005502058],"category_scores_gemma":[0.03068213,0.0009766282,0.002158429,0.001565236,0.0013309,0.001663829,0.001421615,0.002323596,0.0007064188],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001622188,"about_ca_system_score_gemma":0.001557364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02712393,"about_ca_topic_score_gemma":0.02234432,"domain_scores_codex":[0.9962785,0.002776543,0.0001457163,0.0003463948,0.0002659989,0.0001868457],"domain_scores_gemma":[0.9710385,0.02572575,0.001142085,0.0008171626,0.001018575,0.0002579879],"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":[0.0001104624,0.00007205125,0.003216596,0.00009609963,0.0001120941,0.0001623747,0.0002906012,0.9050226,0.0003155508,0.07350164,0.000577437,0.01652251],"study_design_scores_gemma":[0.000006998617,0.0000104806,0.0001655432,0.00001062121,0.000006289544,0.000007765536,0.00001219979,0.9903468,0.00004094303,0.009152172,0.0002330781,0.000007034523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03392623,0.0002632347,0.963654,0.0002290268,0.00004701609,0.0001685115,0.0001863058,0.0003519226,0.001173683],"genre_scores_gemma":[0.6158357,0.0005528503,0.3752625,0.000164931,0.00009371807,0.001565422,0.001072386,0.0002043416,0.005248066],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02712393,"threshold_uncertainty_score":0.05393207,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3686365064289348,"score_gpt":0.5523188855137922,"score_spread":0.1836823790848574,"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."}}