Bibliographic record
Abstract
There are over 60 peer-reviewed reports of cerebral blood flow (CBF) changes during exercise. A recurrent finding is that CBF increases by 10–30% from rest to ∼60% of peak oxygen uptake, and thereafter decreases towards, or even below, baseline as exercise intensity progresses. The reduction in CBF at later stages of exercise occurs despite progressive increases in neuronal activity and cerebral perfusion pressure, which normally act to elevate CBF. These CBF dynamics are best explained by a hyperventilation-induced hypocapnia. Heat stress-induced hyperventilation, both at rest and during exercise, is also known to further exacerbate cerebral vasoconstriction and hence reduce CBF. How exercise, in combination with heat stress and/or dehydration, may influence cerebral substrate delivery and metabolism is poorly established. A theoretical schematic diagram based on data from Trangmar et al. (2014) representing changes in cerebral perfusion pressure (CPP), partial pressure of arterial CO2 (), cerebral blood flow (CBF), cerebral oxygen delivery (DO2), cerebral oxygen extraction fraction (OEF), and the cerebral metabolic rate of oxygen (CMRO2) throughout incremental exercise in dehydrated (red lines) and euhydrated (black lines) conditions. Note that in both conditions there is a modest decrease in CBF and DO2 at higher levels of exercise intensity despite increases in CPP, mediated primarily through reductions in ; however, CMRO2 is unchanged due to proportional increases in OEF. As discussed, the increase in cerebral oxygen extraction required to maintain CMRO2 under combined stresses of exercise and dehydration of 3% body mass reduction is clearly much less than its potential limit. Naturally many future questions arise, including the consideration of independent manipulations of CBF via , temperature and dehydration, with the measurement of cerebral SNA outflow. The work by Trangmar and colleagues provides clear direction for these future studies. None declared.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".