A systematic review and meta-analysis on pharmacist-led interventions for the management of peptic ulcer disease
Bibliographic record
Abstract
Pharmacists are essential for developing pharmacotherapy plans, conducting clinical assessments, and overseeing drug monitoring. Their interventions help prevent medication errors and adverse drug events and enhance medication safety. This study aimed to systematically review pharmacist-led interventions for managing medication-related issues in patients receiving anti-ulcer treatments. A systematic review and meta-analysis was performed to explore four databases for studies published from 1904 up to June 2024. Nine studies were reviewed, including four retrospective, three case-control, one mixed-method, and one prospective pre-post study involving 34,099 participants. The average age of the patients was 61 years, and 50.23% were male. The study quality was high, with an average score of 6.22/7 on the modified Newcastle-Ottawa scale. All studies involved direct interactions between pharmacists and patients or physicians, and data were primarily collected from hospital electronic records. Pooled analysis demonstrated that pharmacist interventions significantly improved the rational use of anti-ulcer medications (OR: 4.5; 95% CI: 0.97 to 20.80; I2 = 89%, P = 0.05), as reported by studies. Pharmacist interventions have a significant impact on improving rational drug use, reducing costs and treatment duration, and enhancing appropriate medication use. These interventions also positively influenced medication adherence and the correction of irrational drug use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.023 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".