{"id":"W2107324201","doi":"10.1177/0272989x04267008","title":"Refining Estimates of Major Depression Incidence and Episode Duration in Canada Using a Monte Carlo Markov Model","year":2004,"lang":"en","type":"article","venue":"Medical Decision Making","topic":"Mental Health Research Topics","field":"Psychology","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Incidence (geometry); Duration (music); Depression (economics); Statistics; Markov chain Monte Carlo; Markov model; Monte Carlo method; Econometrics; Computer science; Medicine; Markov process; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.006698499,0.000395569,0.0008655677,0.001847619,0.0008885057,0.001447531,0.001434795,0.0006274511,0.002028077],"category_scores_gemma":[0.04109819,0.0005656015,0.0008909807,0.001928458,0.0006451267,0.0008557963,0.0008372344,0.0009671338,0.0001492704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01131985,"about_ca_system_score_gemma":0.01329063,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8352947,"about_ca_topic_score_gemma":0.7792028,"domain_scores_codex":[0.9978797,0.001170723,0.0001163675,0.0002904636,0.0003414958,0.0002011914],"domain_scores_gemma":[0.9797267,0.01608515,0.001145404,0.0006500951,0.002115413,0.000277211],"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":[0.0003542018,0.00007657774,0.09722891,0.0001355645,0.0003312033,0.0001475842,0.0005076746,0.8447663,0.0003169431,0.01790137,0.001755492,0.03647817],"study_design_scores_gemma":[0.00004043839,0.00002654633,0.01503071,0.00004490556,0.00006319163,0.00003827564,0.00007997636,0.9776024,0.000153405,0.006006975,0.0008882467,0.00002489798],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5770556,0.001312139,0.4111249,0.001471681,0.00004540804,0.0005282522,0.002577085,0.000472745,0.005412236],"genre_scores_gemma":[0.9191805,0.0004891374,0.07750708,0.000094456,0.00001739144,0.0001925761,0.001302088,0.00003464353,0.001182076],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1647053,"threshold_uncertainty_score":0.3313506,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05948164965937775,"score_gpt":0.4307853803543425,"score_spread":0.3713037306949648,"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."}}