Plasma hyperosmolality improves tolerance to combined heat stress and central hypovolemia in humans
Bibliographic record
Abstract
Heat stress profoundly impairs tolerance to central hypovolemia in humans via a number of mechanisms including heat-induced hypovolemia. However, heat stress also elevates plasma osmolality; the effects of which on tolerance to central hypovolemia remain unknown. This study examined the effect of plasma hyperosmolality on tolerance to central hypovolemia in heat-stressed humans. With the use of a counterbalanced and crossover design, 12 subjects (1 female) received intravenous infusion of either 0.9% iso-osmotic (ISO) or 3.0% hyperosmotic (HYPER) saline. Subjects were subsequently heated until core temperature increased ~1.4°C, after which all subjects underwent progressive lower-body negative pressure (LBNP) to presyncope. Plasma hyperosmolality improved LBNP tolerance (ISO: 288 ± 193 vs. HYPER: 382 ± 145 mmHg × min, P = 0.04). However, no differences in mean arterial pressure ( P = 0.10), heart rate ( P = 0.09), or muscle sympathetic nerve activity ( P = 0.60, n = 6) were observed between conditions. When individual data were assessed, LBNP tolerance improved ≥25% in eight subjects but remained unchanged in the remaining four subjects. In subjects who exhibited improved LBNP tolerance, plasma hyperosmolality resulted in elevated mean arterial pressure (ISO: 62 ± 10 vs. HYPER: 72 ± 9 mmHg, P < 0.01) and a greater increase in heart rate (ISO: +12 ± 24 vs. HYPER: +23 ± 17 beats/min, P = 0.05) before presyncope. No differences in these variables were observed between conditions in subjects that did not improve LBNP tolerance (all P ≥ 0.55). These results suggest that plasma hyperosmolality improves tolerance to central hypovolemia during heat stress in most, but not all, individuals.
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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) | 0.001 | 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) | 0.001 | 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".