Postexercise rehydration: potassium-rich drinks versus water and a sports drink
Bibliographic record
Abstract
Fluid retention, thirst quenching, tolerance, and palatability of different drinks were assessed. On 4 different days, 12 healthy, physically active volunteers (24.4 ± 3.2 years old, 74.75 ± 11.36 kg body mass (mean ± S.D)), were dehydrated to 2.10% ± 0.24% body mass by exercising in an environmental chamber (32.0 ± 0.4 °C dry bulb, 53.8 ± 5.2% relative humidity). Each day they drank 1 of 4 beverages in random order: fresh coconut water (FCW), bottled water (W), sports drink (SD), or potassium-rich drink (NEW); volume was 120% of weight loss. Urine was collected and perceptions self-reported for 3 h. Urine output was higher (p < 0.05) for W (894 ± 178 mL) than SD (605 ± 297 mL) and NEW (599 ± 254 mL). FCW (686 ± 250 mL) was not different from any other drink (p > 0.05). Fluid retention was higher for SD than W (68.2% ± 13.0% vs. 51.3% ± 12.6%, p = 0.013), but not for FCW and NEW (62.5% ± 15.4% and 65.9% ± 15.4%, p > 0.05). All beverages were palatable and well tolerated; none maintained a positive net fluid balance after 3 h, but deficit was greater in W versus SD (p = 0.001). FCW scored higher for sweetness (p = 0.03). Thirst increased immediately after exercise but returned to baseline after drinking a small volume (p < 0.0005). In conclusion, additional potassium in FCW and NEW did not result in additional rehydration benefits over those already found in a conventional sports drink with sodium.
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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 | 0.001 |
| 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) | 0.002 | 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".