Effect of nonsteroidal anti-inflammatory drugs with varying extent of COX-2–COX-1 selectivity on urinary sodium and potassium excretion in the rat
Bibliographic record
Abstract
Nonsteroidal anti-inflammatory drugs (NSAIDs) have different selectivity to inhibit cyclooxygenase-1 (COX-1) and COX-2. Treatment with NSAIDs has been associated with kidney side effects. We compared the effect of a selected group of NSAIDs with different COX-2--COX-1 selectivities on urinary sodium and potassium excretion in rats. Each treatment with rofecoxib, celecoxib, meloxicam, diclofenac, and flurbiprofen (30, 120, 9, 30, and 125 mg/kg, respectively) and placebo was administered orally once daily for 4 days. Urine was collected 0-8 h after each dose. Urinary sodium and potassium excretion and urine flow rate were compared with placebo. As compared with placebo, rofecoxib, celecoxib, diclofenac, and flurbiprofen significantly reduced excretion rate of sodium (rofecoxib, 0.28 +/- 0.02 vs. 0.41 +/- 0.03; celecoxib, 0.23 +/- 0.03 vs. 0.48 +/- 0.04; diclofenac, 0.09 +/- 0.02 vs. 0.46 +/- 0.03; and flurbiprofen, 0.11 +/- 0.02 vs. 0.47 +/- 0.02 micromol/(min x 100 g)) and potassium (rofecoxib, 0.55 +/- 0.04 vs. 0.68 +/- 0.04; celecoxib, 0.50 +/- 0.06 vs. 0.72 +/- 0.06; diclofenac, 0.26 +/- 0.05 vs. 0.67 +/- 0.04; and flurbiprofen, 0.35 +/- 0.05 vs. 0.62 +/- 0.03 micromol/ (min x 100 g)). Rofecoxib and flurbiprofen significantly reduced urine flow rate. Meloxicam had no significant effect on either sodium and potassium excretion or on the urine flow rate. At the examined dosage level, no relationship was found between reported COX-2--COX-1 selectivity and urinary electrolytes excretion.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".