318 VALIDATION OF THE FRAMINGHAM GENERAL CARDIOVASCULAR RISK PREDICTION SCORE IN A MULTI-ETHNIC PRIMARY CARE COHORT
Bibliographic record
Abstract
Objectives: Cardiovascular disease (CVD) risk prediction charts either over or under-estimated risk depending on the country in which the charts were used. Their usefulness in the Asia-Pacific region is not known. This study examines the validity of the new Framingham general CVD risk chart in a multi-ethnic population. Methods: This is a 10 year retrospective study of randomly selected patients attending a primary care clinic. Baseline CVD risk factors were captured from patient records. Each patient's CVD score was computed from these parameters. All CVD events occurring from 1998-2007 were counted Results: 1136 patient records were studied. In 1998, mean age was 56.1years(SD±9) 34.7% men, 8.2% smokers, 56.2% diabetics and 57.6% on anti-hypertensive treatment. Mean BP, hdl-cholesterol and ldl-cholesterol was 140.3/85.2mmHg, 1.23, 4.09mmol/L respectively. Mean CVD points for men was 17.8 giving a CVD Risk of 29.4% and for women 16.3, CVD risk 16.8%. CVD events occurred in 97(24.6%) men and 103(13.9%) women over the 10years Conclusion: Taking into account that this cohort are already receiving treatment, the Framingham General CVD Risk Prediction Score predicts quite accurately the 10-year CVD Risk. In the absence of local risk prediction charts, the Framingham chart is a reliable alternative.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".