{"id":"W2988372504","doi":"10.1002/bimj.201900046","title":"Berkson's paradox and weighted distributions: An application to Alzheimer's disease","year":2019,"lang":"en","type":"article","venue":"Biometrical Journal","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Aging; Canadian Institutes of Health Research","keywords":"Mathematics; Statistics; Inference; Population; Statistical inference; Sample (material); Correlation; Econometrics; Demography; Computer science; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004583247,0.0001279916,0.0002203227,0.0003820241,0.0001273404,0.0001494889,0.0001801633,0.00006465171,0.0002034239],"category_scores_gemma":[0.001033583,0.00009738284,0.00004940915,0.001326948,0.00004530253,0.0001303097,0.00005601903,0.0001865373,0.00009396432],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004448173,"about_ca_system_score_gemma":0.00004071287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003875712,"about_ca_topic_score_gemma":2.337528e-7,"domain_scores_codex":[0.998726,0.0001360697,0.0003081847,0.0002443207,0.0003308952,0.0002545469],"domain_scores_gemma":[0.9978901,0.000780264,0.000107457,0.0002363433,0.0001332932,0.0008525801],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001000382,0.000361389,0.0122633,0.00002119252,0.00005083457,0.00001877918,0.00002961281,1.19983e-7,0.0004133726,0.467676,0.001438155,0.5176272],"study_design_scores_gemma":[0.0004748115,0.0003358354,0.1266798,0.00003629913,0.0001438295,0.00006427863,0.00002376161,0.002676106,0.0001684967,0.8631566,0.005956828,0.0002833934],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04960421,0.0002896706,0.9486986,0.0007703896,0.0001206794,0.0002707463,0.00009282029,0.0000336067,0.0001192681],"genre_scores_gemma":[0.5092873,0.00002937064,0.4903756,0.0001102767,0.0001488793,0.00001436212,0.000009396929,0.0000122072,0.00001253893],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5173438,"threshold_uncertainty_score":0.3971157,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04853374036936612,"score_gpt":0.3790857080481019,"score_spread":0.3305519676787357,"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."}}