Effect of Human Skin Grafts on Whole-Body Heat Loss During Exercise Heat Stress
Bibliographic record
Abstract
When exposed to heat stress, increases in cutaneous blood flow and sweating in well-healed grafted skin are severely attenuated, which could impair whole-body heat loss if skin grafts cover a large portion of total body surface area (TBSA). It is unknown to what extent whole-body heat loss is impaired when skin grafts cover a significant (eg, >50%) proportion of TBSA. The authors examined whole-body heat exchange during and after 60 min of cycling exercise in the heat (35°C; 25% relative humidity), at a fixed rate of metabolic heat production (~400 W) in a woman (age, 36 years; mass, 78.2 kg) with well-healed (17+ years) skin grafts covering 75% of TBSA. Her responses were compared with two noninjured control subjects. Whole-body evaporative and dry heat exchange were measured by direct calorimetry. While exercising in the same ambient conditions and at the same rate of heat production, relative evaporative heat loss of nongrafted skin in the grafted subject (ie, evaporative heat loss per m) was nearly twice that of the control subjects. However, total rate of evaporative heat loss reached only 59% of the amount required for heat balance in the skin-grafted subject compared with 92 ± 3% in controls. Thus, the increase in core temperature was 2-fold greater for the grafted (1.22°C) vs control (0.61 ± 0.19°C) individuals. This case study demonstrates that a large area of grafted skin greatly diminishes maximum evaporative heat loss during exercise in the heat, making a compensable environment for control subjects uncompensable for skin-grafted 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) | 0.000 | 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) | 0.001 | 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".