Regional cerebral pulsatile hemodynamics during isocapnic and poikilocapnic hyperthermia in young men
Bibliographic record
Abstract
Abstract Hyperthermia is known to induce hypocapnia‐driven reductions in cerebral blood flow; however, it is unknown if it causes changes in hemodynamic pulsatility that negatively influence cerebrovascular function. This retrospective analysis aimed to assess cerebrovascular hemodynamic pulsatile buffering (damping factor; DFi) during poikilocapnic (HT) and isocapnic (HT‐C) hyperthermia. We hypothesized that HT would reduce cerebral DFi, while HT‐C would attenuate the reduction in DFi by limiting increases in resistance. Ten healthy males were passively heated +2°C from normothermia (BL). Blood flow through the internal carotid artery (ICA) and vertebral artery (VA) was measured using vascular ultrasound. Blood velocity through the middle cerebral artery (MCA) and the posterior cerebral artery (PCA) was measured using transcranial ultrasound. DFi was calculated as the ratio of proximal to distal pulsatility index (PI): Anterior cerebral DFi = PIICA/PIMCA; Posterior cerebral DFi = PIVA/PIPCA. Anterior DFi decreased in both HT (1.08 ± 0.19 a.u; p = 0.007) and HT‐C (1.12 ± 0.231 a.u; p = 0.021) conditions from BL values (1.27 ± 0.14 a.u). No changes were observed in posterior DFi, p = 0.116. Irrespective of PaCO2 clamping, both hyperthermic conditions reduced anterior DFi, suggesting other mechanisms are responsible for cerebrovascular hemodynamic buffering. Posterior DFi responses were not observed, suggesting preferential buffering of the hyperthermic posterior circulation (VA–PCA).
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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 | 0.000 |
| 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".