Fluid Balance and Dehydration in the Young Athlete
Bibliographic record
Abstract
Many young athletes train and compete under conditions that put their body fluid balance at risk, and hypohydration is usually the major concern. Another less frequent condition is hyperhydration that—if accompanied by other risk factors—may cause hyponatremia. Water and electrolyte losses during physical activities occur primarily from sweat. Such losses have been identified mostly in active (but nonathletic) young populations under laboratory settings. Studies have been trying to estimate fluid losses in the athletic population under field conditions, taking into account the sport modality and environmental conditions. Besides these external conditions, young athletes adopt different drinking attitudes, which may depend on knowledge, education, and the opportunities to drink during the break periods as well as fluid availability. Focusing on the young athlete, this review will discuss water and sodium losses from sweat, the effects of hypohydration on performance, and fluid intake attitudes within and during practices and competitions. Some considerations related to the methods of identifying hydration status and guidelines are also given, with the understanding that they should be individually adapted for the athlete and activity. The young athlete, parents, coaches, and athletic/health professionals should be aware of such information to prevent fluid imbalances and the consequent hazardous effects on performance and health.
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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 | 0.001 | 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".