Prevalence and incidence of gastroduodenal ulcers during treatment with vascular protective doses of aspirin
Bibliographic record
Abstract
BACKGROUND: Aspirin is valuable for preventing vascular events, but information about ulcer frequency is necessary to inform risk-benefit decisions in individual patients. AIM: To determine ulcer prevalence and incidence in a population representative of those given aspirin therapy and evaluate risk predictors. METHODS: Patients taking aspirin 75-325 mg daily were recruited from four countries. Exclusions included use of gastroprotectant drugs or other non-steroidal anti-inflammatory drugs. We measured point prevalence of endoscopic ulcers, after quantitating dyspeptic symptoms. Incidence was assessed 3 months later in those eligible to continue (no baseline ulcer or reason for gastroprotectants). RESULTS: In 187 patients, ulcer prevalence was 11% [95% confidence interval (CI) 6.3-15.1%]. Only 20% had dyspeptic symptoms, not significantly different from patients without ulcer. Ulcer incidence in 113 patients followed for 3 months was 7% (95% CI 2.4-11.8%). Helicobacter pylori infection increased the risk of a duodenal ulcer [odds ratio (OR) 18.5, 95% CI 2.3-149.4], as did age >70 for ulcers in stomach and duodenum combined (OR 3.3, 95% CI 1.3-8.7). CONCLUSIONS: Gastroduodenal ulcers are found in one in 10 patients taking low-dose aspirin, and most are asymptomatic; this needs considering when discussing risks/benefits with patients. Risk factors include older age and H. pylori (for duodenal ulcer).
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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 | 0.004 |
| 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".