Underutilization of gastroprotective strategies in aspirin users at increased risk of upper gastrointestinal complications
Bibliographic record
Abstract
BACKGROUND: Aspirin use is with an increased risk of upper gastrointestinal complications (UGICs). Proton pump inhibitors (PPIs) decrease the risk of UGICs among aspirin users. The distribution of risk factors for UGIC and PPI utilization among aspirin users remains uncharacterized. AIM: To determine the prevalence and predictors of PPI use in high-risk aspirin users. METHODS: Using questionnaires and administrative records, we collected information on aspirin and PPI utilization and risk factors for UGICs from a stratified random sample of subjects with established cardiovascular disease. We calculated the proportion of aspirin users with UGIC risk factors and determined the prevalence of PPI use among aspirin users with risk factors. Regression analysis was used to determine predictors of PPI use among aspirin users. RESULTS: Overall response rate was 35%, of whom 86% were regular aspirin users. Seventy-one per cent of aspirin users had at least one risk factor (in addition to cardiac disease) for the development of UGICs. Although a history of UGIC was predictive of PPI use, 44% of aspirin users with a prior history of UGICs did not receive a concomitant PPI, and only 23% of subjects with additional UGIC risk factors were prescribed a PPI. CONCLUSION: There is a high prevalence of UGIC risk factors among aspirin users, and many are not prescribed PPIs as a gastroprotective strategy.
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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 | 0.008 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".