{"id":"W2324675669","doi":"10.1023/b:ejep.0000032369.60873.60","title":"Using Administrative Healthcare Data to Recruit Study Subjects: Experience with ‘Camouflaged Sampling’","year":2004,"lang":"en","type":"article","venue":"European Journal of Epidemiology","topic":"Smoking Behavior and Cessation","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ministry of Health; Centre for Advancing Health Outcomes; University of British Columbia","funders":"","keywords":"Medicine; Stratified sampling; Socioeconomic status; Sampling (signal processing); Medical prescription; Receipt; Agonist; Population; Environmental health; Family medicine; Demography; Internal medicine; Nursing","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":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.3374817,0.001382636,0.001867001,0.003062234,0.00576103,0.00441477,0.008107236,0.008441487,0.004041433],"category_scores_gemma":[0.4986548,0.001694998,0.00152011,0.004866221,0.01002844,0.004570179,0.006160303,0.005300798,0.001348822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003834853,"about_ca_system_score_gemma":0.007699817,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02365545,"about_ca_topic_score_gemma":0.02397501,"domain_scores_codex":[0.4902773,0.4573592,0.01341186,0.007457439,0.02911418,0.00237998],"domain_scores_gemma":[0.3039577,0.6088804,0.01862649,0.03777456,0.02803202,0.002728838],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00102868,0.001054736,0.1601282,0.006794692,0.0007360352,0.001881418,0.09374039,0.002329282,0.003718519,0.02049893,0.07033566,0.6377535],"study_design_scores_gemma":[0.00146982,0.004672227,0.2948704,0.0172981,0.001116581,0.01348781,0.08348273,0.0248235,0.01361157,0.07347172,0.4706713,0.001024209],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4368933,0.02186492,0.3337846,0.1561356,0.005315204,0.01155405,0.001124295,0.0008542395,0.03247374],"genre_scores_gemma":[0.6134774,0.007235661,0.2584255,0.1026637,0.002863546,0.008328401,0.0008350288,0.0005440309,0.005626662],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6625183,"threshold_uncertainty_score":0.817003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6860492324055646,"score_gpt":0.5285902399536031,"score_spread":0.1574589924519615,"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."}}