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 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 | 0.000 |
| 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) | 0.001 | 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".