{"id":"W3093632891","doi":"10.1515/em-2019-0021","title":"A comparison of approaches for estimating combined population attributable risks (PARs) for multiple risk factors","year":2020,"lang":"en","type":"article","venue":"Epidemiologic Methods","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; McMaster University; Impact; Alberta Health Services","funders":"Canadian Cancer Society","keywords":"Statistics; Econometrics; Population; Estimation; Attributable risk; Risk factor; Relative risk; Risk assessment; Mathematics; Computer science; Medicine; Environmental health; Economics; Confidence interval","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.006837143,0.0002964719,0.001670911,0.00005413342,0.0001784119,0.00001004841,0.0002853406,0.0002793673,0.00001268021],"category_scores_gemma":[0.2208407,0.0002319908,0.0002906526,0.0001862819,0.00007714101,0.0001240484,0.00009765008,0.0002801942,4.330166e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000609191,"about_ca_system_score_gemma":0.00001622864,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000104237,"about_ca_topic_score_gemma":0.000004560088,"domain_scores_codex":[0.9961793,0.001481768,0.001348877,0.0004729356,0.0001063899,0.0004107632],"domain_scores_gemma":[0.9425761,0.05525341,0.001603254,0.0003231798,0.0001308369,0.000113219],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004807586,0.0003563587,0.7591751,0.001541461,0.0002296138,1.34789e-7,0.001705088,0.04720313,0.003443451,0.1004117,0.003132662,0.08232053],"study_design_scores_gemma":[0.000304018,0.0004170997,0.002009791,0.00002352277,0.00007887056,1.28802e-7,0.0001882636,0.5540435,0.01519654,0.4275101,0.00007377268,0.0001544011],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08347224,0.00007455648,0.9136994,0.0001703241,0.00009371851,0.001912112,0.0001601033,0.0003869209,0.00003057296],"genre_scores_gemma":[0.4025147,0.000001964995,0.5969813,0.00002878369,0.00004805076,0.0002884475,0.0001084568,0.00002331571,0.00000490388],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7571654,"threshold_uncertainty_score":0.9460312,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8704649646198399,"score_gpt":0.6146697198316243,"score_spread":0.2557952447882156,"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."}}