Salt + Glycerol-Induced Hyperhydration Enhances Fluid Retention More Than Salt- or Glycerol-Induced Hyperhydration
Bibliographic record
Abstract
Hyperhydration has been demonstrated to improve work capacity and cardiovascular and thermoregulatory functions, enhance orthostatic tolerance, slow or neutralize bone demineralization, and decrease postdive bubble formation. Adding sodium or glycerol to a hyperhydration solution optimizes fluid retention. Sodium and glycerol produce their effect through different physiological mechanisms. If combined into a hyperhydration solution, their impact on fluid retention could potentially be greater than their singular effect. We compared the effect of salt-induced hyperhydration (SIH), glycerol-induced hyperhydration (GIH), and salt + glycerol-induced hyperhydration (SGIH) on fluid balance responses during a 3-hr passive experiment. Using a randomized, crossover, and counterbalanced experiment, 15 young men (22 ± 4 years) underwent three, 3-hr hyperhydration experiments during which they ingested 30 ml/kg fat-free mass (FFM) of water with an artificial sweetener plus either (a) 7.5 g of table salt/L (SIH), (b) 1.4 g glycerol/kg FFM (GIH), or (c) 7.5 g of table salt/L + 1.4 g glycerol/kg FFM (SGIH). After 3 hr, there were no significant differences in plasma volume changes among experiments (SIH: 11.3% ± 9.9%; GIH: 7.6% ± 12.7%; SGIH: 11.3% ± 13.7%). Total urine production was significantly lower (SIH: 775 ± 329 ml; GIH: 1,248 ± 270 ml; SGIH: 551 ± 208 ml) and fluid retention higher (SIH: 1,127 ± 212 ml; GIH: 729 ± 115 ml; SGIH: 1,435 ± 140 ml) with SGIH than either GIH or SIH. Abdominal discomfort was low and not significantly different among experiments. In conclusion, results show that SGIH reduces urine production and provides more fluid retention than either SIH or 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) | 0.000 | 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".