Predictors of gastroduodenal erosions in patients taking low‐dose aspirin
Bibliographic record
Abstract
BACKGROUND: Gastroduodenal ulcers are common in patients taking low-dose aspirin. However, the factors predisposing to mucosal erosions, the precursor lesions, are not well known. AIMS: To examine the potential risk factors for the development of erosions in patients chronically taking low-dose aspirin. METHODS: Patients included were taking aspirin 75-325 mg daily for >28 days. Exclusion criteria included use of nonsteroidal anti-inflammatory and ulcer-healing drugs. Demographic data were collected at baseline, prior to endoscopy to determine the frequency and number of erosions and Helicobacter pylori status. In those without ulcer or other exclusions, endoscopy was repeated at 3 months. RESULTS: Fewer patients had gastric erosions if they were H. pylori +ve (48.5% vs. 66.4% in H. pylori-ve patients at baseline, P = 0.17; 40.0% vs. 64.1% at 3 months, P = 0.029). If gastric erosions were present, they were also less numerous in H. pylori +ve patients (3.61 +/- 0.83 vs. 4.90 +/- 0.53 at baseline, P = 0.026; 2.17 +/- 0.68 vs. 5.68 +/- 0.86 at 3 months, P = 0.029). There was a trend (0.1 > P > 0.05) for more gastric erosions in those taking >100 mg/day aspirin. Males had more duodenal erosions at baseline (25.2% vs. 7.5%, P = 0.016). Patient age did not affect the presence or number of erosions. H. Pylori was not significantly associated with duodenal erosion numbers. CONCLUSIONS: Helicobacter pylori infection may partially protect against low-dose aspirin-induced gastric erosions; damage to the stomach appears weakly dose-related; and older age does not increase the risk of erosions.
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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 | 0.003 |
| 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) | 0.003 | 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".