Non-steroidal anti-inflammatory drugs and the risk of end stage renal disease in hypertensive individuals
Bibliographic record
Abstract
Objective. To examine the association between non-steroidal anti-inflammatory drug (NSAID) use and end stage renal disease (ESRD) among hypertensive subjects. Study design. We conducted a nested case-control study within a cohort of 77,887 hypertensive adult subjects within the province of Saskatchewan, Canada. Outcome. The primary outcome was ESRD, defined by chronic dialysis or renal transplantation. Exposure. NSAID exposure was determined using prescription records, for various time windows up to 10 years preceding the onset of end stage renal disease. Statistical analysis. Rate ratios (RR) were estimated with 95% confidence intervals using conditional logistic regression, adjusting for potential confounding variables and stratified for effect modifiers. Results. We identified 397 cases and 7,399 controls. In subjects followed for at least 10 years continuous NSAID use was observed in 20.8% of cases and 17.9% of controls (RR = 1.18, 95% CI 0.68--2.05). Additionally, neither early (RR = 1.10, 95% CI 0.50--2.41) nor late (RR = 0.81, 95% CI 0.32--2.04) NSAID exposure was associated with ESRD during this time period. Evaluation of other time windows (0--2 years, 2--5 years and 5--10 years) and NSAID dosing provided similar results. Results were not modified by loop diuretic and angiotensin converting enzyme inhibitor use. Conclusion. Up to 10 years of non-steroidal anti-inflammatory drug use does not appear to influence the development of end stage renal disease. These results however may be influenced by unmeasured co-morbidities and confounding by "contra-indication".
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".