Time-Dependent Changes in Atherosclerotic Plaque Composition in Patients Undergoing Carotid Surgery
Bibliographic record
Abstract
BACKGROUND: Time-dependent trends in the incidence of cardiovascular disease have been reported in high-income countries. Because atherosclerosis underlies the majority of cardiovascular diseases, we investigated temporal changes in the composition of atherosclerotic plaques removed from patients undergoing carotid endarterectomy. METHODS AND RESULTS: The Athero-Express study is an ongoing, longitudinal, vascular biobank study that includes the collection of atherosclerotic plaques of patients undergoing primary carotid endarterectomy in the province of Utrecht from 2002 to 2011. Histopathologic features of plaques of 1583 patients were analyzed in intervals of 2 years. The analysis included quantification of collagen, calcifications, lipid cores, plaque thrombosis, macrophages, smooth muscle cells, and microvessels. Large atheroma, plaque thrombosis, macrophages, and calcifications were less frequently observed over time, with adjusted odds ratios of 0.72 (95% confidence interval, 0.650-0.789), 0.62 (95% confidence interval, 0.569-0.679), 0.87 (95% confidence interval, 0.800-0.940), and 0.75 (95% confidence interval, 0.692-0.816) per 2-year increase in time, respectively. These changes in plaque characteristics were consistently observed in patient subgroups presenting with stroke, transient ischemic attack, ocular symptoms, and asymptomatic patients. Concomitantly, risk factor management and secondary prevention strategies among vascular patients scheduled for carotid endarterectomy significantly improved over the past decade. CONCLUSIONS: In conclusion, over the past decade, atherosclerotic plaques harvested during carotid endarterectomy show a time-dependent change in plaque composition characterized by a decrease in features currently believed to be causal for plaque instability. This appears to go hand in hand with improvements in risk factor management.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".