Effect of glycerol-induced hyperhydration on thermoregulatory and cardiovascular functions and endurance performance during prolonged cycling in a 25°C environment
Bibliographic record
Abstract
We compared the effect of glycerol-induced hyperhydration (GIH) to that of water-induced hyperhydration (WIH) on cardiovascular and thermoregulatory functions and endurance performance (EP) during prolonged cycling in a temperate climate in subjects consuming fluid during exercise. At weekly intervals, 6 trained male subjects ingested, in a randomized, double-blind, counterbalanced fashion, either a glycerol (1.2 g glycerol/kg bodyweight (BW) with 26 mL/kg BW of water-aspartame-flavored fluid) or placebo solution (water-aspartame-flavored fluid only) over a 2 h period. Subjects then performed 2 h of cycling at 66% of the maximal oxygen consumption (VO(2) max) and 25 degrees C while drinking 500 mL/h of sports drink, which was followed by a step-incremented cycling test to exhaustion. Levels of hyperhydration did not differ significantly between treatments before exercise. During exercise, GIH significantly reduced urine production by 246 mL. GIH did not increase sweat rate nor did it decrease heart rate, rectal temperature, or perceived exertion during exercise as compared with WIH. EP was not significantly different between treatments. Neither treatment induced undesirable side effects. It is concluded that, compared with WIH, GIH decreases urine production, but does not improve cardiovascular or thermoregulatory functions, nor does it improve EP during 2 h of cycling in a 25 degrees C environment in trained athletes consuming 500 mL/h of fluid during exercise.
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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".