Use of low-dose acetylsalicylic acid for cardiovascular disease prevention: A practical, stepwise approach for pharmacists
Bibliographic record
Abstract
Low-dose acetylsalicylic acid (ASA) is recommended in patients with established cardiovascular disease. However, the role of ASA in those without cardiovascular disease (i.e., primary prevention) is less clear, which has led to discordance among Canadian guidelines. In 2018, 3 double-blind, randomized controlled trials were published that evaluated ASA 100 mg daily versus placebo in patients without established cardiovascular disease. In the ASPREE trial, ASA did not reduce the risk of all-cause death, dementia, or persistent physical disability in patients ≥70 years of age but increased the risk of major bleeding. In the ARRIVE trial, ASA failed to lower the risk of a composite of cardiovascular events but increased any gastrointestinal bleeding in patients at intermediate risk of cardiovascular disease. In the ASCEND trial, ASA significantly reduced the primary composite cardiovascular outcome in patients with diabetes for a number needed to treat of 91 over approximately 7.4 years. Yet major bleeding was increased with ASA for a number needed to harm of 112. Therefore, in most situations, ASA should not be recommended for primary cardiovascular prevention. However, there are additional indications for ASA beyond cardiovascular disease. Thus, a sequential algorithm was developed based on contemporary evidence to help pharmacists determine the suitability of ASA in their patients and play an active role in educating their patients about the potential benefits (or lack thereof) and risks of ASA. Can Pharm J (Ott) 2020;153:xx-xx.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".