Should patients prescribed long-term low-dose aspirin receive proton pump inhibitors? A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Several clinical guidelines recommend the use of proton pump inhibitors (PPIs) in patients taking low-dose aspirin but report no or limited supporting data. We conducted a systematic review and meta-analysis to examine the effects of co-administration of PPIs in patients taking low-dose aspirin on the risks of adverse gastrointestinal (GI) and cardiovascular (CV) events, and on patient adherence to aspirin. METHODS: We searched PUBMED, EMBASE and Cochrane Central Register of Controlled Trials databases for relevant articles published through November 2013. We included randomised controlled trials (RCTs) and observational studies in patients taking low-dose aspirin with and without PPIs. Risk of bias was assessed using the Cochrane Collaboration's tool (for RCTs) and the Newcastle-Ottawa Scale (for observational studies). Pooled risk ratios (RRs) were computed using a random-effects model. RESULTS: We included 13 studies, of which 12 (2 RCTs and 10 observational studies) reported on GI events, and one (cohort study) on both GI bleeding and CV events. No study reported on adherence to aspirin. Co-administration of PPIs in patients receiving low-dose aspirin was associated with risk reductions of 73% (RR 0.27, 95% CI 0.17-0.42) and 50% (RR 0.50, 95% CI 0.32-0.80) in the occurrence of peptic ulcer and GI bleeding respectively. There was evidence of bias in publications reporting on the GI events. CONCLUSIONS: The practice of co-prescribing PPIs in patients taking low-dose aspirin is supported by some data, but the evidence is rather weak. It currently remains unclear whether the benefits of co-administration of PPIs in users of low-dose aspirin outweigh their potential harms.
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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 | 0.021 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.040 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".