{"id":"W4387503967","doi":"10.1186/s12874-023-02044-x","title":"Adjusting for Berkson error in exposure in ordinary and conditional logistic regression and in Poisson regression","year":2023,"lang":"en","type":"article","venue":"BMC Medical Research Methodology","topic":"Occupational and environmental lung diseases","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; BC Cancer Agency; Centre Hospitalier de l’Université de Montréal","funders":"National Cancer Institute; National Institutes of Health","keywords":"Statistics; Job-exposure matrix; Logistic regression; Poisson regression; Regression; Poisson distribution; Regression analysis; Econometrics; Mathematics; Medicine; Population; Environmental health; Confidence interval","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.008197061,0.0001093555,0.0003689293,0.0005876241,0.00006495963,0.000004931253,0.00007288114,0.000259716,0.0001677319],"category_scores_gemma":[0.03491741,0.00007948856,0.00002769306,0.0004222092,0.0004708151,0.00005251012,0.0002143322,0.0005264707,0.000006684087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001077589,"about_ca_system_score_gemma":0.0002458412,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002060041,"about_ca_topic_score_gemma":0.0006435087,"domain_scores_codex":[0.9960314,0.002097455,0.0003448903,0.0004130774,0.000669594,0.0004435543],"domain_scores_gemma":[0.9812875,0.01823706,0.0000389864,0.0001081146,0.000030477,0.0002978235],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00424132,0.0002070139,0.9598449,0.0008637012,0.000006299183,0.0005770618,0.0001910625,0.00002793053,0.002387892,0.0004792399,0.001110366,0.03006323],"study_design_scores_gemma":[0.002960612,0.0004870475,0.9778022,0.001290469,0.000007607553,0.00005236461,0.000798604,0.007537948,0.00007934075,0.008722231,0.0001884238,0.00007319249],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9942589,0.001493951,0.0004651482,0.003123871,0.00005894417,0.0004925892,0.00001883982,0.00001558564,0.00007215388],"genre_scores_gemma":[0.9865595,0.0007599597,0.01157522,0.000152485,0.0001269942,0.0002176533,0.000261533,0.00001503975,0.0003316528],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02999004,"threshold_uncertainty_score":0.9732119,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5829241814341569,"score_gpt":0.5801489863611682,"score_spread":0.002775195072988734,"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."}}