{"id":"W2890155095","doi":"10.1177/0962280218797145","title":"Eliminating systematic bias from case-crossover designs","year":2018,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; Government of Alberta; Alberta Health Services","funders":"Health Research Board","keywords":"Crossover; Statistics; Confounding; Calibration; Crossover study; Computer science; Econometrics; Confidence interval; Meta-analysis; Publication bias; Medicine; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","sts","insufficient_payload"],"consensus_categories":["metaresearch","insufficient_payload"],"category_scores_codex":[0.1960624,0.0003266512,0.001266093,0.0009191419,0.0005375929,0.0006885874,0.002219333,0.0004846388,0.02719697],"category_scores_gemma":[0.7269284,0.0002262316,0.000124785,0.003496544,0.003816064,0.0002734472,0.001259226,0.001989783,0.001937683],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003594226,"about_ca_system_score_gemma":0.0006038671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001094246,"about_ca_topic_score_gemma":0.0001475893,"domain_scores_codex":[0.9206273,0.06157307,0.002759452,0.001668247,0.01172322,0.001648706],"domain_scores_gemma":[0.6248158,0.3704985,0.0002550768,0.00151374,0.001466176,0.001450727],"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":[0.0004960027,0.0005061928,0.0007454312,0.0006701403,0.00006680835,0.01451436,0.004072761,0.000002721893,0.008685268,0.08475431,0.006890724,0.8785953],"study_design_scores_gemma":[0.001633283,0.001272342,0.001641971,0.002499367,0.00003041674,0.0007567715,0.01073465,0.2413982,0.01387341,0.7244318,0.001076051,0.000651786],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008436683,0.000411984,0.9736367,0.0004712381,0.0007372853,0.0008995623,0.00006567203,0.00004881214,0.01529202],"genre_scores_gemma":[0.1664638,0.000009323682,0.8321209,0.0002874515,0.0003441118,0.0001607014,0.000003054968,0.00004109311,0.000569539],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8779435,"threshold_uncertainty_score":0.998895,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7550744409644745,"score_gpt":0.7229459907852953,"score_spread":0.0321284501791792,"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."}}