Comparison of Five Major Guidelines for Statin Use in Primary Prevention
Bibliographic record
Abstract
Letters3 July 2018Comparison of Five Major Guidelines for Statin Use in Primary PreventionAdrienne J. Lindblad, BSP, ACPR, PharmD and Christina Korownyk, MD, CCFPAdrienne J. Lindblad, BSP, ACPR, PharmDUniversity of Alberta, Edmonton, Alberta, Canada (A.J.L., C.K.)Search for more papers by this author and Christina Korownyk, MD, CCFPUniversity of Alberta, Edmonton, Alberta, Canada (A.J.L., C.K.)Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/L18-0181 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail TO THE EDITOR:Mortensen and Nordestgaard (1) compare 5 major guidelines on statin use in the prevention of primary atherosclerotic cardiovascular disease (ASCVD) in the Danish population. Although the authors outline how many patients would be eligible for statin therapy per guideline, we believe that their estimates of how many ASCVD events could be prevented by following each guideline are unrealistic. Patients ultimately decide whether they will take medications, and—outside of clinical trials—adherence to statin therapy remains low (2). None of the guidelines in the study provides adequate information (that is, individualized, quantified risks and benefits) to allow for shared, ...References1. Mortensen MB, Nordestgaard BG. Comparison of five major guidelines for statin use in primary prevention in a contemporary general population. Ann Intern Med. 2018;168:85-92. [PMID: 29297004]. doi:10.7326/M17-0681 LinkGoogle Scholar2. Lemstra M, Blackburn D, Crawley A, Fung R. Proportion and risk indicators of nonadherence to statin therapy: a meta-analysis. Can J Cardiol. 2012;28:574-80. [PMID: 22884278] doi:10.1016/j.cjca.2012.05.007 CrossrefMedlineGoogle Scholar3. Allan GM, Lindblad AJ, Comeau A, Coppola J, Hudson B, Mannarino M, et al. Simplified lipid guidelines: prevention and management of cardiovascular disease in primary care. Can Fam Physician. 2015;61:857-67. [PMID: 26472792] MedlineGoogle Scholar4. Karalis DG, Victor B, Ahedor L, Liu L. Use of lipid-lowering medications and the likelihood of achieving optimal LDL-cholesterol goals in coronary artery disease patients. Cholesterol. 2012;2012:861924. [PMID: 22888414] doi:10.1155/2012/861924 CrossrefMedlineGoogle Scholar5. McCartney M, Treadwell J, Maskrey N, Lehman R. Making evidence based medicine work for individual patients. BMJ. 2016;353:i2452. [PMID: 27185764] doi:10.1136/bmj.i2452 CrossrefMedlineGoogle Scholar Author, Article, and Disclosure InformationAffiliations: University of Alberta, Edmonton, Alberta, Canada (A.J.L., C.K.)Disclosures: Authors have disclosed no conflicts of interest. Forms can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=L18-0181. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoComparison of Five Major Guidelines for Statin Use in Primary Prevention in a Contemporary General Population Martin Bødtker Mortensen and Børge Grønne Nordestgaard Comparison of Five Major Guidelines for Statin Use in Primary Prevention Martin Bødtker Mortensen and Børge Nordestgaard Comparison of Five Major Guidelines for Statin Use in Primary Prevention Philippe Giral Metrics 3 July 2018Volume 169, Issue 1Page: 66-67KeywordsAtherosclerotic cardiovascular diseaseCholesterolDecision makingDisclosureHeartLipidsLow density lipoproteinRelative riskStatinsTreatment guidelines ePublished: 3 July 2018 Issue Published: 3 July 2018 Copyright & PermissionsCopyright © 2018 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.037 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".