Estimated Sweat Loss, Fluid and Carbohydrate Intake, and Sodium Balance of Male Major Junior, AHL, and NHL Players During On-Ice Practices
Bibliographic record
Abstract
Several previous studies have reported performance decrements in team sport athletes who dehydrated approximately 1.5-2% of their body mass (BM) through sweating. This study measured on-ice sweat loss, fluid intake, sodium balance, and carbohydrate (CHO) intake of 77 major junior (JR; 19 ± 1 years), 60 American Hockey League (AHL; 24 ± 4 years), and 77 National Hockey League (NHL; 27 ± 5 years) players. Sweat loss was calculated from pre- to post-exercise BM plus fluid intake minus urine loss. AHL (2.03 ± 0.62 L/hr) and NHL (2.02 ± 0.74 L/hr) players had higher sweat rates (p < .05) than JR players (1.63 ± 0.58 L/hr). AHL (1.23 ± 0.69%; p = .006) and NHL (1.29% ± 0.63%; p < .001) players had ∼30% greater BM losses than JR players (0.89% ± 0.57%). There was no difference in fluid intake between groups (p > .05). Sodium deficits (sodium loss - intake) were greater (p < .05) in AHL (1.68 ± 0.74 g/hr) and NHL (1.56 ± 0.84 g/hr) players compared with JR players (1.01 ± 0.50 g/hr). CHO intake was similar between groups (14-20 g CHO/hr), with 29%, 32%, and 40% of JR, AHL, and NHL players consuming no CHO, respectively. In summary, sweat rates were high in all players, but the majority of players (74/77, 54/60, and 68/77 of JR, AHL, and NHL, respectively) avoided mild dehydration (>2% BM) during 60 min of practice. However, ∼15%, 41%, and 48% of the JR, AHL, and NHL players, respectively, may have reached mild dehydration and increased risk of performance decrements in a 90-min practice.
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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 | 0.001 | 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".