Effect of food intake on the ventilatory response to increasing core temperature during exercise
Bibliographic record
Abstract
Food intake increases metabolism and body temperature, which may in turn influence ventilatory responses. Our aim was to assess the effect of food intake on ventilatory sensitivity to rising core temperature during exercise. Nine healthy male subjects exercised on a cycle ergometer at 50% of peak oxygen uptake in sessions with and without prior food intake. Ventilatory sensitivity to rising core temperature was defined by the slopes of regression lines relating ventilatory parameters to core temperature. Mean skin temperature, mean body temperature (calculated from esophageal temperature and mean skin temperature), oxygen uptake, carbon dioxide elimination, minute ventilation, alveolar ventilation, and tidal volume (VT) were all significantly higher at baseline in sessions with food intake than without food intake. During exercise, esophageal temperature, mean skin temperature, mean body temperature, carbon dioxide elimination, and end-tidal CO2 pressure were all significantly higher in sessions with food intake than without it. By contrast, ventilatory parameters did not differ between sessions with and without food intake, with the exception of VT during the first 5 min of exercise. The ventilatory sensitivities to rising core temperature also did not differ, with the exception of an early transient effect on VT. Food intake increases body temperature before and during exercise. Other than during the first 5 min of exercise, food intake does not affect ventilatory parameters during exercise, despite elevation of both body temperature and metabolism. Thus, with the exception of an early transient effect on VT, ventilatory sensitivity to rising core temperature is not affected by food intake.
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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) | 0.001 | 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".