{"id":"W3185203671","doi":"10.1016/j.jclinepi.2021.07.010","title":"A modeling approach to derive baseline risk estimates for GRADE recommendations: Concepts, development, and results of its application to the American Society of Hematology 2019 guidelines on prevention of venous thromboembolism in surgical hospitalized patients","year":2021,"lang":"en","type":"article","venue":"Journal of Clinical Epidemiology","topic":"Venous Thromboembolism Diagnosis and Management","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University; McMaster University; Impact","funders":"National Institute for Health and Care Research","keywords":"Guideline; Baseline (sea); Medicine; Venous thromboembolism; Risk assessment; Transparency (behavior); Confidence interval; Intensive care medicine; Risk analysis (engineering); Internal medicine; Computer science; Pathology","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":[],"category_scores_codex":[0.01143358,0.0009882282,0.001054647,0.002409262,0.0007253091,0.002621755,0.001687147,0.00113932,0.003308421],"category_scores_gemma":[0.03971851,0.0007012576,0.00223211,0.002031258,0.0002932825,0.001345117,0.0008038239,0.001877019,0.0007109176],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002287506,"about_ca_system_score_gemma":0.003977875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0276972,"about_ca_topic_score_gemma":0.0217467,"domain_scores_codex":[0.9953547,0.003065621,0.0002899683,0.0005880513,0.0005485184,0.0001531401],"domain_scores_gemma":[0.9761296,0.02012671,0.001444595,0.0006674048,0.001472606,0.0001592091],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004839301,0.0007632014,0.06887192,0.0003585223,0.001546396,0.0002127359,0.0007190478,0.6783477,0.001048259,0.06875184,0.008420671,0.1704758],"study_design_scores_gemma":[0.00009552841,0.0002392737,0.007403748,0.0001206366,0.0003425563,0.0001242388,0.0001421753,0.9609155,0.0004738934,0.02720646,0.00287718,0.00005874315],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05701313,0.0003063508,0.9344297,0.001085076,0.0000522114,0.0007189938,0.00279359,0.0006869007,0.002914062],"genre_scores_gemma":[0.4381905,0.0003586327,0.5556009,0.0002409337,0.00005050637,0.001580851,0.002137654,0.000106497,0.001733626],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9885664,"threshold_uncertainty_score":0.06046724,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1948602315632268,"score_gpt":0.490098712396345,"score_spread":0.2952384808331182,"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."}}