Medications for community pharmacists to dose adjust or avoid to enhance prescribing safety in individuals with advanced chronic kidney disease: a scoping review and modified Delphi
Bibliographic record
Abstract
BACKGROUND: Community pharmacists commonly see individuals with chronic kidney disease (CKD) and are in an ideal position to mitigate harm from inappropriate prescribing. We sought to develop a relevant medication list for community pharmacists to dose adjust or avoid in individuals with an estimated glomerular filtration rate (eGFR) below 30 mL/min informed through a scoping review and modified Delphi panel of nephrology, geriatric and primary care pharmacists. METHODS: A scoping review was undertaken to identify higher risk medications common to community pharmacy practice, which require a dose adaptation in individuals with advanced CKD. A 3-round modified Delphi was conducted, informed by the medications identified in our scoping review, to establish consensus on which medications community pharmacists should adjust or avoid in individuals with stage 4 and 5 CKD (non-dialysis). RESULTS: Ninety-two articles and 88 medications were identified from our scoping review. Of which, 64 were deemed relevant to community pharmacy practice and presented for consideration to 27 panel experts. The panel consisted of Canadian pharmacists practicing in nephrology (66.7%), geriatrics (18.5%) and primary care (14.8%). All participants completed rounds 1 and 2 and 96% completed round 3. At the end of round 3, the top 40 medications to adjust or avoid were identified. All round 3 participants selected metformin, gabapentin, pregabalin, non-steroidal anti-inflammatory drugs, nitrofurantoin, ciprofloxacin and rivaroxaban as the top ranked medications. CONCLUSION: Medications eliminated by the kidneys may accumulate and cause harm in individuals with advanced chronic kidney disease. This study provides an expert consensus of the top 40 medications that community pharmacists should collaboratively adjust or avoid to enhance medication safety and prescribing for individuals with an eGFR below 30 mL/min.
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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 | 0.145 | 0.228 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.019 | 0.012 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".