Regional changes in brain blood flow during severe passive hyperthermia: effects of Pa<sub>CO<sub>2</sub></sub> and extracranial blood flow
Bibliographic record
Abstract
We investigated 1) the regional distribution of cerebral blood flow (CBF), 2) the influence of end-tidal Pco2 (PetCO2) on CBF, and 3) the potential for an extracranial blood "steal" from the anterior brain region during passive hyperthermia. Nineteen (13 male) volunteers underwent supine passive heating until a steady-state esophageal temperature of 2°C above resting was established. Measurements were obtained 1) during normothermia (Normo), 2) during poikilocapnic hyperthermia (Hyper), and 3) during hyperthermia with PetCO2 and end-tidal Po2 clamped to Normo levels (Hyper-clamp). Blood flow in the internal carotid (Qica), vertebral (QVA), and external carotid (Qeca) arteries (Duplex ultrasound), blood velocity of the middle cerebral (MCAv) and posterior cerebral (PCAv) arteries (transcranial Doppler), and cutaneous vascular conductance on the cheek (cheek CVC; Doppler velocimetry) were measured at each stage. During Hyper, PetCO2 was lowered by 7.0 ± 5.2 mmHg, resulting in a reduction in Qica (-18 ± 17%), Qva (-31 ± 21%), MCAv (-22 ± 13%), and PCAv (-18 ± 10%) compared with Normo (P < 0.05). The reduction in QVA was greater than that in QICA (P = 0.017), MCAv (P = 0.047), and PCAv (P = 0.034). Blood flow/velocity was completely restored in each intracranial vessel (ICA, VA, MCA, and PCA) during Hyper-clamp. Despite a ∼250% increase in QECA and a subsequent increase in cheek CVC during Hyper compared with Normo, reductions in QICA were unrelated to changes in QECA. These data provide three novel findings: 1) hyperthermia attenuates QVA to a greater extent than QICA, 2) reductions in CBF during hyperthermia are governed primarily by reductions in arterial Pco2, and 3) increased QECA is unlikely to compromise QICA during 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 | 0.000 |
| 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".