Effects of a 24-h naproxen dose on hydration and electrolyte measures during moderate-intensity cycling in the heat
Bibliographic record
Abstract
Few controlled laboratory studies have examined the negative effects non-steroidal anti-inflammatory drugs can have on fluid–electrolyte balance during exercise. Our objective was to determine whether a 24-h naproxen dose negatively affected hydration and electrolyte measures before, during, and 3 h after 90 min of cycling in a hot or ambient environment. Using a double blind, randomized and counterbalanced cross-over design, 11 volunteers (six male, five female) completed four trials, with conditions as follows: (1) placebo and ambient, (2) placebo and heat, (3) naproxen and ambient, and (4) naproxen and heat. We found no statistically significant differences among experimental conditions for any dependent measures. Though not statistically significant, mean fluid volume was higher and urine volume was lower during naproxen trials compared with placebos. Mean aggregate plasma sodium was <135 mmol/L at all time points and did not significantly change over time. Overall plasma potassium significantly increased pre- (3.9 ± 0.4) to post-exercise (4.2 ± 0.4 mmol/L, p = 0.02). In conclusion, an acute naproxen dose did not significantly alter hydration–electrolyte balance. The trend for naproxen to increase fluid volume and decrease urine volume suggests the start of fluid retention, which should concern individuals at risk for hyponatremia or with pre-existing cardiovascular conditions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".