{"id":"W3004656358","doi":"10.1016/j.clnu.2020.01.024","title":"Overcoming confounding by indication in nutrition research using electronic healthcare data","year":2020,"lang":"en","type":"editorial","venue":"Clinical Nutrition","topic":"Nutrition and Health in Aging","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"NIHR Surgical Reconstruction Microbiology Research Centre; Canadian Institutes of Health Research; Abbott Nutrition; Baxter International; Nutricia Research Foundation; Takeda Pharmaceuticals U.S.A.; Fresenius Biotech; Abbott Laboratories","keywords":"Observational study; Confounding; Medicine; Health care; General partnership; Family medicine; MEDLINE","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":["metaepi_narrow","research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.006974041,0.0003846884,0.00137748,0.0007874051,0.0003754007,0.0001326827,0.0006183158,0.002320165,0.00006303997],"category_scores_gemma":[0.006396654,0.000460386,0.0001833296,0.001215887,0.0002816572,0.000399928,0.0003203114,0.008597258,0.0000810409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002218332,"about_ca_system_score_gemma":0.002195966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000310144,"about_ca_topic_score_gemma":0.0001210887,"domain_scores_codex":[0.9914036,0.001568288,0.00236753,0.001667729,0.001824871,0.001167993],"domain_scores_gemma":[0.9932854,0.003512552,0.0006373582,0.001127313,0.0008093614,0.0006280398],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00125866,0.001798689,0.0006869715,0.008889017,0.0000358542,0.00004708333,0.00004033934,4.822356e-8,0.0004566555,0.0001826783,0.9848365,0.001767574],"study_design_scores_gemma":[0.01027232,0.001011101,0.00009557389,0.01303555,0.0001015062,0.00001425383,0.0002292776,0.001625078,0.00003742836,0.003896163,0.9693149,0.000366805],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.02096259,0.04225307,0.0009057383,0.08090956,0.835451,0.01432218,0.004095344,0.000691061,0.0004094334],"genre_scores_gemma":[0.02702808,0.06886551,0.003490489,0.001516537,0.8471182,0.0003219575,0.05141231,0.0001836707,0.0000632494],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.07939302,"threshold_uncertainty_score":0.9997848,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4048578094139637,"score_gpt":0.6031981409196928,"score_spread":0.1983403315057292,"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."}}