The Relationship Between Clinical Pharmacy and Adequate Medication Reconciliation and Acute Rejection Treatment for Renal Transplant Patients
Bibliographic record
Abstract
The purpose of this systematic review is to determine how clinical transplant pharmacists affect changes in medication reconciliation and changes in rejection. A database search was done using PubMed, Scopus, Embase, Cochrane Review, and clinicaltrials.gov, and the final number of papers in the review included 4 studies. Eligible studies included adult kidney transplant recipients in the US, Canada, Brazil, or Australia and were published in English. The intervention was any pharmacist-led intervention, and the outcomes of interest were number of medication reconciliations completed and number of acute rejections. Included studies show that clinical transplant pharmacist interventions contributed to more medication reconciliations, with fewer medication errors occurring. Also, there were fewer acute rejections when patients received pharmaceutical care. All data was statistically significant (p <0.05) except the data provided for one of the acute rejection studies, which had a p-value of 0.213. However, the results from this review were inconclusive given the validity of the studies included in the review and the high level of bias in each study. There is potential for pharmacist-led interventions to positively impact medication reconciliation and acute rejection in kidney transplant patients and this review emphasizes the need for future research on this topic.
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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 | 0.008 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".