Medications Used Routinely in Primary Care to be Dose-Adjusted or Avoided in People With Chronic Kidney Disease: Results of a Modified Delphi Study
Bibliographic record
Abstract
Background: Chronic kidney disease (CKD) affects up to 18% of those over the age of 65 years. Potentially inappropriate medication prescribing in people with CKD is common. Objectives: Develop a pragmatic list of medications used in primary care that required dose adjustment or avoidance in people with CKD, using a modified Delphi panel approach, followed by a consensus workshop. Methods: We conducted a comprehensive literature search to identify potential medications. A group of 17 experts participated in a 3-round modified Delphi panel to identify medications for inclusion. A subsequent consensus workshop of 8 experts reviewed this list to prioritize medications for the development of point-of-care knowledge translation materials for primary care. Results: After a comprehensive literature review, 59 medications were included for consideration by the Delphi panel, with a further 10 medications added after the initial round. On completion of the 3 Delphi rounds, 66 unique medications remained, 63 requiring dose adjustment and 16 medications requiring avoidance in one or more estimated glomerular filtration rate categories. The consensus workshop prioritized this list further to 24 medications that must be dose-adjusted or avoided, including baclofen, metformin, and digoxin, as well as the newer SGLT2 inhibitor agents. Conclusion and Relevance: We have developed a concise list of 24 medications commonly used in primary care that should be dose-adjusted or avoided in people with CKD to reduce harm. This list incorporates new and frequently prescribed medications and will inform an updated, easy to access source for primary care providers.
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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 | 0.095 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".