The Determination of the Plaque Burden on the Carotid Artery With Ultrasound Significantly Improves the Risk Prediction in Middle-Aged Subjects Compared to PROCAM: An Outcome Study
Bibliographic record
Abstract
BACKGROUND: There are only few data about the predictive value of atherosclerosis imaging beyond traditional risk calculators in younger subjects. METHODS: We assessed cardiovascular risk prediction with the PROCAM (the Prospective Cardiovascular Munster Study) risk equation and with carotid plaque imaging (determination of total plaque area (TPA) and the maximum plaque thickness with ultrasound) in subjects without known cardiovascular diseases. The follow-up was generated during follow-up examinations as part of preventive medical examinations or by telephone calls. RESULTS: In 2,508 subjects aged 35 - 64 years (50 ± 8 years, 34% women), 132 (5.3%) cardiovascular events occurred (42 myocardial infarction, 17 bypass surgery, 31 stent implantation, 42 coronary artery disease defined by invasive angiography) during a mean follow-up period of 5.4 (1 - 12) years. TPA in combination with the maximum plaque thickness (type III - IV b plaques ) tended to be superior compared to TPA, and both plaque imaging methods were superior to PROCAM: area under the curve (AUC) 0.9 (95% confidence interval (CI): 0.91 - 0.89) vs. 0.89 (95% CI: 0.90 - 0.88), P = 0.2 vs. 0.82 (95% CI: 0.84 - 0.81), P = 0.001; positive predictive value (PPV) 27% (95% CI: 0.31 - 0.22) vs. 19% (95% CI: 0.22 - 0.16) vs.19% (95% CI: 0.27 - 0.13). CONCLUSIONS: Amount of carotid plaque assessed by carotid plaque imaging significantly improves cardiovascular risk prediction beyond the PROCAM risk equation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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 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".