Sodium-induced hyperhydration decreases urine output and improves fluid balance compared with glycerol- and water-induced hyperhydration
Bibliographic record
Abstract
Before 2010, which is the year the World Anti-Doping Agency banned its use, glycerol was commonly used by athletes for hyperhydration purposes. Through its effect on osmoreceptors, we believe that sodium could prove a viable alternative to glycerol as a hyperhydrating agent. Therefore, this study compared the effects of sodium-induced hyperhydration (SIH), glycerol-induced hyperhydration (GIH) and water-induced hyperhydration (WIH) on fluid balance responses. Using a randomized, double-blind and counterbalanced protocol, 17 men (21 ± 3 years, 64 ± 6 kg fat-free mass (FFM)) underwent three 3-h hyperhydration protocols during which they ingested, over the first 60-min period, 30 mL/kg FFM of water with (i) an artificial sweetener (WIH); (ii) an artificial sweetener + 7.45 g/L of table salt (SIH); or (iii) an artificial sweetener + 1.4 g glycerol/kg FFM (GIH). Changes in body weight (BW), urine production, fluid retention, hemoglobin, hematocrit, plasma volume, and perceptual variables were monitored throughout the 3-h trials. After 3 h, SIH was associated with significantly (p < 0.05) lower hemoglobin, hematocrit (SIH: 43.1% ± 2.8%; GIH: 44.9% ± 2.4%), and urine production, as well as greater BW, fluid retention (SIH: 1144 ± 294 mL; GIH: 795 ± 337 mL), and plasma volume (SIH: 11.9% ± 12.0%; GIH: 4.0% ± 6.0%) gains, compared with GIH and WIH. No significant differences in heart rate or abdominal discomfort were observed between treatments. In conclusion, our results indicate that SIH is a superior hyperhydrating technique than, and proves to be a worthwhile alternative to, GIH.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.000 | 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 teacher head, 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".