Plasma volume variation with exercise: a crucial consideration for obese adolescent boys
Bibliographic record
Abstract
Previous studies have demonstrated that considerable plasma volume variations (ΔPV) occur during and after exposure to different environmental and physiological conditions. Such changes have an important effect on plasma concentration of metabolite values. Currently, no study has examined ΔPV in individuals with different body weight status and used ΔPV to correct plasma solute values. The aims of this study were to assess (i) the effect of body weight status on ΔPV and (ii) the impact of these variations on lactate ([La]) and glucose ([Glu]) concentrations in normal-weight, overweight, and obese adolescent boys. Participants performed a cycling sprint test at their maximal power output. ΔPV were calculated using 2 methods, and both lactate and glucose concentrations were compared using total circulating values (T) and corrected values (cr) for ΔPV: [La]T vs. [La]cr and [Glu]T vs. [Glu]cr. Following exercise, ΔPV values decreased significantly from rest value and were higher in obese compared with overweight and normal-weight boys (p < 0.01). Moreover, ΔPV were correlated with body weight status (r = 0.85; p < 0.05). While [La]T and [Glu]T differed among the groups, no difference persisted when these values were corrected for ΔPV. The differences between total circulating and corrected values were significant. The impact of body weight status on ΔPV and thus on various plasma measures in response to exercise is important and should be considered in further studies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".