A Meta-Analysis of the Effects of Glycerol-Induced Hyperhydration on Fluid Retention and Endurance Performance
Bibliographic record
Abstract
The authors determined, through a meta-analytic approach, whether glycerol-induced hyperhydration (GIH) enhances fluid retention and increases endurance performance (EP) significantly more than water-induced hyperhydration (WIH). Collectively, studies administered 23.9 +/- 2.7 mL of fluid/kg body weight (BW) with 1.1 +/- 0.2 g glycerol/kg BW, and hyperhydration was measured 136 +/- 15 min after its onset. Compared with WIH, GIH increased fluid retention by 7.7 +/- 2.8 mL/kg BW (P < 0.01; pooled effect size [PES]: 1.64 +/- 0.80, P < 0.01, N = 14). The use of GIH was associated with an improvement in EP of 2.62% +/- 1.60% (P = 0.047; PES: 0.35 +/- 0.13, P = 0.014, N = 4). Unarguably, GIH significantly enhances fluid retention better than WIH. Because of the dearth of data, the effect of GIH on EP must be further investigated before more definitive conclusions can be drawn as to its ergogenic property.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.018 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".