Adding carotid total plaque area to the Framingham risk score improves cardiovascular risk classification
Bibliographic record
Abstract
INTRODUCTION: Cardiovascular events (CE) due to atherosclerosis are preventable. Identification of high-risk patients helps to focus resources on those most likely to benefit from expensive therapy. Atherosclerosis is not considered for patient risk categorization, even though a fraction of CE are predicted by Framingham risk factors. Our objective was to assess the incremental value of combining total plaque area (TPA) with the Framingham risk score (FramSc) using post-test probability (Ptp) in order to categorize risk in patients without CE and identify those at high risk and requiring intensive treatment. MATERIAL AND METHODS: A descriptive cross-sectional study was performed in the primary care setting in an Argentine population aged 22-90 years without CE. Both FramSc based on body mass index and Ptp-TPA were employed in 2035 patients for risk stratification and the resulting reclassification was compared. Total plaque area was measured with a high-resolution duplex ultrasound scanner. RESULTS: 57% male, 35% hypertensive, 27% hypercholesterolemia, 14% diabetes. 20.1% were low, 28.5% moderate, and 51.5% high risk. When patients were reclassified, 36% of them changed status; 24.1% migrated to a higher and 13.6% to a lower risk level (κ index = 0.360, SE κ = 0.16, p < 0.05, FramSc vs. Ptp-TPA). With this reclassification, 19.3% were low, 18.9% moderate and 61.8% high risk. CONCLUSIONS: Quantification of Ptp-TPA leads to higher risk estimation than FramSc, suggesting that Ptp-TPA may be more sensitive than FramSc as a screening tool. If our observation is confirmed with a prospective study, this reclassification would improve the long-term benefits related to CE prevention.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".