Nonsteroidal Anti-inflammatory Drug (NSAID) Prescriptions in Living Kidney Donors
Bibliographic record
Abstract
Background: Current guidelines recommend that living kidney donors should avoid non-steroidal anti-inflammatory drugs (NSAIDs) due to their potential nephrotoxic effects. It is unclear if physicians are adhering to this guideline recommendation. Methods: We conducted a population-based, retrospective cohort study of adult living kidney donors in Alberta, Canada, who donated between 2002 and 2019. We identified the proportion of living kidney donors who filled an NSAID prescription at least 1-year beyond date of donation. Of those donors who were prescribed an NSAID, we assessed how many underwent post-prescription laboratory testing for kidney function and potassium within 14 days. Results: Of 759 living kidney donors in the study cohort, 273 (36%) had at least one NSAID prescription over a median follow up of 7.2 years (IQR 3.5-11.5).Donors with at least one NSAID prescription were more likely to be from lower socio-economic status, have an earlier donation date, have higher pre-donation eGFR, and have co-morbid gout and osteoarthritis at the index date. The proportion of donors with at least one prescription in follow-up remained stable over time (~10% per year).Family physicians accounted for 66% of all NSAID prescriptions. Of the donors with an NSAID prescription, 30% also filled at least one prescription for an opioid. Approximately, 10% of donors had measurements of serum creatinine or potassium within 14 days of the first NSAID prescription. The risk of acute kidney injury or hyperkalemia was uncommon in those that underwent laboratory testing (6% and 3% respectively). Conclusion: Over one-third of living kidney donors are prescribed NSAIDs despite current guideline recommendations. Few donors had evidence of post-prescription laboratory testing, but adverse outcomes were uncommon in those who were tested. Further research assessing outcomes following NSAID use is recommended to better inform guidelines for living kidney donors.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".