The Clamping of End-Tidal Carbon Dioxide Does Not Influence Cognitive Function Performance During Moderate Hyperthermia With or Without Skin Temperature Manipulation
Bibliographic record
Abstract
Increases in body temperature from heat stress (i.e., hyperthermia) generally impairs cognitive function across a range of domains and complexities, but the relative contribution from skin versus core temperature changes remains unclear. Hyperthermia also elicits a hyperventilatory response that decreases the partial pressure of end-tidal carbon dioxide (PetCO2) and subsequently cerebral blood flow that may influence cognitive function. We studied the role of skin and core temperature along with PetCO2 on cognitive function across a range of domains. Eleven males completed a randomized, single-blinded protocol consisting of poikilocapnia (POIKI, no PetCO2 control) or isocapnia (ISO, PetCO2 maintained at baseline levels) during passive heating using a water-perfused suit (water temperature ~ 49°C) while middle cerebral artery velocity (MCAv) was measured continuously as an index of cerebral blood flow. Cognitive testing was completed at baseline, neutral core-hot skin (37.0 ± 0.2°C-37.4 ± 0.3°C), hot core-hot skin (38.6 ± 0.3°C-38.7 ± 0.2°C), and hot core-cooled skin (38.5 ± 0.3°C-34.7 ± 0.6°C). The cognitive test battery consisted of a detection task (psychomotor processing), 2-back task (working memory), set-shifting and Groton Maze Learning Task (executive function). At hot core-hot skin, poikilocapnia led to significant (both p < 0.05) decreases in PetCO2 (∆−21%) and MCAv (∆−26%) from baseline, while isocapnia clamped PetCO2 (∆ + 4% from baseline) leading to a significantly (p = 0.023) higher MCAv (∆−18% from baseline) compared to poikilocapnia. There were no significant differences in errors made on any task (all p > 0.05) irrespective of skin temperature or PetCO2 manipulation. We conclude that neither skin temperature nor PetCO2 maintenance significantly alter cognitive function during passive hyperthermia.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".