Cardiovascular risk in patients with small and medium abdominal aortic aneurysms, and no history of cardiovascular disease
Bibliographic record
Abstract
BACKGROUND: Cardiovascular disease (CVD) is the main cause of death in people with abdominal aortic aneurysm (AAA). There is little evidence that screening for AAA reduces all-cause or cardiovascular mortality. The aim of the study was to assess whether subjects with a small or medium AAA (3·0-5·4 cm), without previous history of clinical CVD, had raised levels of CVD biomarkers or increased total mortality. METHODS: This prospective study included subjects with a small or medium AAA and controls, all without a history of clinical CVD. CVD biomarkers (high-sensitivity C-reactive protein, hs-CRP; heart-type fatty acid-binding protein, H-FABP) were measured, and survival was recorded. RESULTS: Of a total of 815 people, 476 with an AAA and 339 controls, a cohort of 86 with small or medium AAA (3-5·4 cm) and 158 controls, all with no clinical history of CVD, were identified. The groups were matched for age and sex. The AAA group had higher median (i.q.r.) levels of hs-CRP (2·8 (1·2-6·0) versus 1·3 (0·5-3·5) mg/l; P < 0·001) and H-FABP (4·6 (3·5-6·0) versus 4·0 (3·3-5·1) µg/l; P = 0·011) than controls. Smoking was more common in the AAA group; however, hs-CRP and H-FABP levels were not related to smoking. Mean survival was lower in the AAA group: 6·3 (95 per cent confidence interval (c·i.) 5·6 to 6·9) years versus 8·0 (7·6 to 8·1) years in controls (P < 0·001). Adjusted mortality was higher in the AAA group (hazard ratio 3·41, 95 per cent c·i. 2·11 to 9·19; P < 0·001). CONCLUSION: People with small or medium AAA and no clinical symptoms of CVD have higher levels of hs-CRP and H-FABP, and higher mortality compared with controls. They should continue to receive secondary prevention against CVD.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".