Effectiveness of Pharmacist-led Medication Review in Chronic Pain Management
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effectiveness of pharmacist-led medication review in chronic pain management. MATERIALS AND METHODS: Six electronic databases (Medline, Embase, PsycInfo, CINHAL, CENTRAL, International Pharmaceutical Abstracts) reference lists of retrieved articles and relevant websites were searched for randomized controlled trials published in the English language involving adults with chronic pain. Studies were included if one of the intervention arms had received pharmacist-led medication review independently or as part of a multidisciplinary intervention. Risk of bias was assessed for all the included studies. RESULTS: The search strategy yielded 583 unique articles including 5 randomized controlled trials. Compared with control, meta-analysis showed that participants in the intervention group had: a 0.8-point reduction in pain intensity on a 0 to 10 numerical rating scale at 3 months [95% confidence interval (CI), -1.28 to -0.36] and a 0.7-point reduction (95% CI, -1.19 to -0.20) at 6 months; a 4.84 point (95% CI, -7.38 to -2.29) and -3.82 point (95% CI, -6.49 to -1.14) improvement in physical functioning on a 0- to 68-point function subscale of Western Ontario and McMaster Universities Osteoarthritis Index at 3 and 6 months, respectively; and a significant improvement in patient satisfaction equivalent to a "small to moderate effect." DISCUSSION: Pharmacist-led medication review reduces pain intensity and improves physical functioning and patient satisfaction. However, the clinical significance of these findings remain uncertain due to small effect size and nature of reported data within clinical trials that limits recommendation of wider clinical role of pharmacist in chronic pain management.
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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 | 0.038 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".