Impact of pharmacist-led interventions on medication-related problems among patients treated for cancer: A systematic review and meta-analysis of randomized control trials
Bibliographic record
Abstract
BACKGROUND: Medication-related problems (MRPs) continue to impose a voluminous health impact, particularly among patients on anti-cancer therapy, due to the nature and complexity of the care. Pharmacists have a pivotal role in ensuring the safe, effective, and rational use of medicines in this group of patients. OBJECTIVES: To examine the impact of pharmacist-led interventions in resolving MRPs among patients treated for cancer. METHODS: This systematic review and meta-analysis was conducted and reported following the PRISMA protocol and registered in PROSPERO (Registration number: CRD42022311535). Four database searches, PubMed, EMBASE, Cochrane, and International Pharmaceuticals Abstracts, were systematically searched from August 2022 to January 2023. Only randomized control trials (RCTs) were included. The Cochrane risk of bias assessment tool was used to check the quality of the included studies. The outcome measures were overall MRPs, adherence, medication errors, and adverse drug events (ADEs). Data for meta-analysis were analyzed used using STATA version 17 and standardized mean difference effect sizes were calculated for continuous outcomes and odds ratio for categorical outcomes. RESULTS: Out of the 90 studies screened for eligibility, 20 RCT studies were included for the systematic review and 15 for the meta-analysis. Close to two-thirds of the studies were from Europe (n = 7) and Asia (n = 6). A combination of educational and behavioral intervention strategies were used for a period ranged from 8 days to 12 months. The pharmacist-led intervention improved adherence to treatment by 4.79 times (AOR = 4.79; 95%CI = 2.64, 8.68; p-value<0.0001), reduced the occurrence of ADEs by 1.28 (SMD = -1.28; 95%CI = -0.04-2.52; p-value = 0.04) and decreased the overall MRPs by 0.53 (SMD = -0.53; 95%CI = -0.79, -0.28; p-value<0.0001) compared to control groups. CONCLUSION: This study found out that pharmacist-led interventions can significantly lower MRPs among patients treated for cancer. Hence, a global concerted effort has to be made to integrate pharmacists in a multidisciplinary direct cancer care.
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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 | 0.024 | 0.056 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.043 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".