Novel hydration assessment techniques employing thirst and a water intake challenge in healthy men
Bibliographic record
Abstract
Exploring novel hydration indices is important because no human biomarker has been shown to be incontrovertibly valid in all life situations. The present investigation was designed to identify inexpensive, nontechnical methods to use when self-assessing hydration status. This investigation evaluated the validity and efficacy of 2 novel techniques (i.e., thirst sensation and urine volume) to assess hydration state of 29 active men (mean ± SD; age, 23 ± 4 years; body mass, 76.02 ± 11.94 kg) at rest. Eight combinations of 4 water challenges (4.8, 9.3, 11.0, or 14 mL·kg(-1)) and 2 hydration states (mildly hypohydrated (HY), -2.0%; euhydrated (EU), -0.2% body mass) were employed. First, thirst was linearly related to body water loss, and ratings of thirst distinguished HY from EU (p < 0.001) subsequent to 19 h of controlled food and fluid intake. Second, measurements of urine volume 60 min after consuming a water bolus (11.0 or 14 mL·kg(-1)) were strongly and inversely correlated with entering hydration state, assessed by urine specific gravity (r(2) = 0.76, p < 0.0001) and urine osmolality (r(2) = 0.77, p < 0.0001). We concluded that healthy men can employ simple measurements of morning thirst sensation and urine volume to identify the presence of mild hypohydration and to guide fluid replacement. These 2 techniques are relevant because HY (-2% body mass) is the approximate threshold for the onset of thirst, reduced endurance exercise performance, and decrements of working memory and mood.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".