Practical use of the Framingham risk score in primary prevention: Canadian perspective.
Bibliographic record
Abstract
OBJECTIVE: To review the 2009 Canadian Cardiovascular Society guidelines and provide practical recommendations for physicians. SOURCES OF INFORMATION: Initial review of the references provided with the guidelines led to a search of the PubMed, ACP Journal Club, and Cochrane databases using the key words primary prevention and statin for English language clinical trials, randomized controlled trials, meta-analyses, and reviews conducted with human participants. References from appropriate retrieved articles were also reviewed. MAIN MESSAGE: The guidelines outline low-density lipoprotein cholesterol (LDL-C) thresholds and targets to inform optimal use of statins in the primary prevention of cardiovascular disease (CVD). Family history of CVD and levels of high-sensitivity C-reactive protein (hsCRP) are risk modifiers in calculating the risk score with the new recommendations. An electronic calculator has been developed to facilitate increased uptake of these guidelines. Large numbers of asymptomatic people, particularly the elderly, will become eligible for statin therapy according to these new guidelines. Poor uptake by physicians and patients might result from the need for repeated testing of hsCRP and LDL-C levels in people who do not perceive themselves to be ill. Controversy persists concerning the role of hsCRP in the reclassification of CVD risk, and the concept of treating LDL-C to target has never been tested as an independent variable in a randomized trial. As two-thirds of the LDL-C lowering achieved by a statin occurs at the initial dose, it might be possible to achieve considerable CVD risk reduction for those at risk by treating initially with a mid-dose statin without LDL-C follow-up. CONCLUSION: A simplified approach might appeal to patients or physicians who find current guidelines too complex, cumbersome, or costly. Success in getting high-risk patients to take statins is key to achieving improved CVD mortality reduction.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".