Sports drinks do not increase acute kidney injury risk in males during industrial work in the heat when euhydration is maintained, a randomized crossover trial
Bibliographic record
Abstract
Industrial workers regularly perform physical labor under high heat stress, which may place them at risk for dehydration and acute kidney injury. Current guidelines recommend that workers should consume sports drinks to maintain euhydration during work shifts. However, the impact of fructose sweetened sports drinks on acute kidney injury risk is unknown. The purpose of this study was to investigate the effects of sports drink consumption on markers of acute kidney injury following simulated industrial work in the heat. Twenty males completed two matched 2 h simulated industrial work trial visits in a warm and humid environment (30 °C and 55% relative humidity). During and following the bout of simulated work, participants consumed either a commercially available sports drink or a noncaloric placebo. Urine and blood samples, collected pre-, post-, and 16 h post-work were assayed for markers of hydration (plasma/urine osmolality, and urine specific gravity) and acute kidney injury (KIM-1 and NGAL). There were no differences in physiological or perceptual responses to the bout of work (interaction p > 0.05 for all indices), and markers of hydration were similar between trials (interaction p > 0.05 for all indices). KIM-1 (Placebo: Δ Ln 1.18 ± 1.64; Sports drink: Δ Ln 1.49 ± 1.10 pg/mL; groupwide d = 0.89, p < 0.001) and NGAL (Placebo: Δ Ln 0.44 ± 1.11; Sports drink: Δ Ln 0.67 ± 1.22 pg/mL; groupwide d = 0.39, p = 0.03) were elevated pre- to post-work, but there were no differences between trials (interaction p > 0.05). These data provide no evidence that consumption of fructose sweetened sports drinks increases the risk of acute kidney injury during physical work in the heat.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".