{"id":"W4390080088","doi":"10.1101/2023.12.19.23300248","title":"Variations in the results of nutritional epidemiology studies due to analytic flexibility: Application of specification curve analysis to red meat and all-cause mortality","year":2023,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Nutritional Studies and Diet","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Impact; McMaster University","funders":"National Institutes of Health; Arnold Ventures; Yale University","keywords":"Covariate; National Health and Nutrition Examination Survey; Hazard ratio; Observational study; Statistics; Epidemiology; Medicine; Hazard; Quantile; Red meat; Econometrics; Mathematics; Computer science; Demography; Environmental health; Population; Internal medicine; Pathology; Biology; Confidence interval","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":[0.5453202,0.002777213,0.004949836,0.01311332,0.001102907,0.00907269,0.006752546,0.003394315,0.004707892],"category_scores_gemma":[0.8101471,0.002316817,0.02304785,0.01521695,0.005418909,0.007406564,0.008352891,0.004731015,0.0007596861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003887651,"about_ca_system_score_gemma":0.007286059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004238493,"about_ca_topic_score_gemma":0.003311861,"domain_scores_codex":[0.2863698,0.5911679,0.07451452,0.01532701,0.03144937,0.001171387],"domain_scores_gemma":[0.089035,0.827446,0.03389914,0.03510087,0.01419924,0.0003198342],"domain_codex":"methods","domain_gemma":"methods","domain_candidate":"methods","domain_consensus":"methods","study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004231363,0.0001653342,0.1203311,0.1735485,0.234805,0.0009575825,0.009785106,0.02303487,0.001766764,0.06114169,0.02211966,0.3481129],"study_design_scores_gemma":[0.004575323,0.002619351,0.0970426,0.1510471,0.1848024,0.00293582,0.006244658,0.05441903,0.009384033,0.3624356,0.1228778,0.001616261],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03824267,0.1915228,0.7235516,0.01553212,0.001919464,0.007882006,0.01080993,0.001558481,0.00898091],"genre_scores_gemma":[0.5186773,0.03405036,0.407921,0.01168123,0.00099145,0.01854666,0.005902019,0.00121412,0.001015848],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4546798,"threshold_uncertainty_score":0.5607013,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3092570601146397,"score_gpt":0.4499977559443348,"score_spread":0.1407406958296952,"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."}}