Effectiveness of pharmacist-led interventions in improving gout outcomes: A systematic review
Bibliographic record
Abstract
BACKGROUND: Pharmacists are core members of the healthcare team, offering essential support beyond dispensing of medications. They play a crucial role in managing and educating patients, particularly those with chronic diseases. Although numerous studies have reported the involvement of pharmacists in the care of people living with gout, the overall evidence remains limited and is not well defined. OBJECTIVES: This systematic review aims to evaluate the impact of pharmacist-led interventions in improving gout outcomes in people living with gout, specifically focusing on serum uric acid levels, medication adherence, and patient education. METHODS: A comprehensive search was conducted on multiple electronic databases including PubMed, Ovid Embase and Cochrane (library) central to identify relevant studies published up to April 2025. The studies eligible for this review included randomized controlled trials (RCTs), and non-randomized studies (non-RCTs), including pre-post studies and cohort designs that assessed pharmacist-led interventions in the management of gout. Outcomes of interest were reductions in serum uric acid levels, prevention of gout flares, absolute serum uric acid reductions, required dosage to achieve target serum uric acid level, improvements in patient education and frequency of clinic visits. The risk of bias for RCTs was assessed by utilizing the ROB-2 tool, while non-randomized studies were assessed with the ROBINS-I tool. RESULTS: Five studies involving a total of 1805 people living with gout were included in this review. In these studies, pharmacists delivered interventions such as provided interventions; enhancing patient education or providing pharmaceutical care either alone or in collaboration with other healthcare team members were included. Two studies were RCTs, while three were non-RCTs. Pharmacist-led interventions contributed to achieving target serum uric acid levels, determining appropriate the dose of urate lowering therapy needed to attain target levels, improving adherence, and reducing gout flares. The randomized trials were found to have lower risk compared to non-randomized studies. CONCLUSION: The findings suggested that pharmacist involvement in gout management can considerably enhance disease control and improve the overall quality of life for patients. Further research is warranted to identify the most effective components of pharmacist-led interventions and to evaluate their effect on gout outcomes.
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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".