Water-induced hyperhydration increases total body water to a greater extent than glycerol-induced hyperhydration: a case study of a trained triathlete.
Bibliographic record
Abstract
Glycerol-induced hyperhydration (GIH) prior to endurance exercise is a strategy that is increasingly used by athletes. Compared with water-induced hyperhydration (WIH), GIH has been shown to reduce diuresis, thereby increasing total body water (TBW). It has never been demonstrated that WIH proved to be more efficient than GIH for increasing TBW. Therefore, we report the case of a trained triathlete in whom WIH, compared with GIH, increased TBW during a 110-min hydration protocol. On two separate days the subject ingested, in a randomized double blind fashion, either 26 ml.kg(-1) body mass (BM) of water or 26 ml.kg(-1) BM of water with 1.2 g glycerol.kg(-1) BM. Compared with GIH, WIH increased TBW by an additional 511 ml. It is proposed that WIH was effective in decreasing urine output and, therefore, in augmenting TBW, because the water ingested during this treatment was integrated into the body fluid pools relatively more slowly than that ingested during GIH. Practically, this finding implies that it could thus be possible for researchers and athletes to find out that on occasion WIH increases TBW more than GIH over a period of hydration of 2 h.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".