Bibliographic record
Abstract
The United States Preventive Services Task Force (USPSTF) recently recommended that physicians discuss using acetylsalicylic acid for primary prevention of coronary artery events with patients whose risk over 10 years is estimated to be >6%. A recent Critical Appraisal article in Canadian Family Physician found, as well, that ASA is effective for this purpose. Relative risk reduction with ASA is 28%; possible adverse effects include increases in gastrointestinal bleeding and hemorrhagic stroke. As with many preventive measures, integrating recommendations into practice requires tools and organization. This Practice Tip discusses how I am implementing the USPSTF’s recommendations into my practice. Higher-risk patients can benefi t most from ASA. A good way to stratify coronary risk is by using the Framingham equation, which has been endorsed by the authors of the recent Canadian guideline on dyslipidemia as well as the USPSTF. I have found that the easiest way to calculate a patient’s risk with the Framingham equation is with my hand-held computer; I do it with a program called StatCoder (www.statcoder.com). To use the Framingham equation, you input a patient’s age, sex, blood pressure, total cholesterol and high-density lipoprotein cholesterol levels, and whether he or she smokes. I do this during annual physical examinations. I have patients’ previous lipid levels on my chart, and I check their current blood pressure. Calculating their risk then takes no more than a few seconds. The Framingham risk can also be calculated manually, using a table available at http://www.cmaj.ca/cgi/content/ full/162/10/1441/T216, and a calculator for personal computers is available at http://www.nzgg.org.nz/ library/gl_complete/bloodpressure/appendix.cfm#app3. I then enter patients’ risk score on the preventive health tables I use. Those tables can be found at http: //members.rogers.com/mgreiver/tables.htm. If their risk of coronary artery disease over the next 10 years is >6%, I give my patients a handout I have prepared with their risk and absolute risk reduction written in (Figure 1). (A copy is available on-line at http://members.rogers.com/mgreiver/asachemo prophylaxis.htm.) I ask patients to let me know at the next visit if they have chosen to take prophylactic ASA, and I note their decisions on their cumulative patient profi les. This preventive measure is inexpensive and likely to benefi t many patients. Currently available software tools make calculating absolute cardiovascular risk easy in the offi ce. Giving patients this information can help them make informed decisions about taking ASA to prevent heart disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.008 |
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".