{"id":"W2913588687","doi":"10.1002/pds.4701","title":"Conditional validation sampling for consistent risk estimation with binary outcome data subject to misclassification","year":2019,"lang":"en","type":"article","venue":"Pharmacoepidemiology and Drug Safety","topic":"Reliability and Agreement in Measurement","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Carleton University; Institute of Population and Public Health; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Statistics; Contingency table; Categorical variable; Sampling (signal processing); Sample size determination; Simple random sample; Stratified sampling; Sampling design; Estimator; Odds ratio; Econometrics; Computer science; Medicine; Mathematics; Population","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":[0.1392305,0.001110032,0.002071087,0.001946293,0.001026039,0.001498845,0.003127996,0.001503095,0.002060096],"category_scores_gemma":[0.3831755,0.0009456429,0.002231459,0.001674872,0.003317502,0.001705293,0.002694879,0.002276472,0.0003318226],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001681105,"about_ca_system_score_gemma":0.003020206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004845653,"about_ca_topic_score_gemma":0.003035645,"domain_scores_codex":[0.8766598,0.1109178,0.002290448,0.004185098,0.005373767,0.0005731949],"domain_scores_gemma":[0.5626011,0.3856209,0.0173744,0.02625851,0.007200314,0.0009448031],"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":[0.002232583,0.0003392772,0.09535313,0.001178029,0.002243218,0.0006756451,0.001296438,0.4748324,0.002251307,0.2451689,0.003905383,0.1705237],"study_design_scores_gemma":[0.0002918064,0.0004214149,0.008778677,0.0004446261,0.0002826527,0.000270821,0.00007597062,0.8853257,0.003013515,0.09888516,0.002135436,0.00007430473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01601083,0.0001931216,0.9826041,0.00013929,0.00003450185,0.0004072566,0.00009436857,0.0001586342,0.0003578438],"genre_scores_gemma":[0.4565521,0.000263875,0.5397416,0.0003517198,0.00007455689,0.001880041,0.0006356199,0.00009455303,0.0004059878],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1392305,"threshold_uncertainty_score":0.7363303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3722356889622388,"score_gpt":0.482635052189559,"score_spread":0.1103993632273202,"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."}}