Cerebrovascular and cardiovascular responses to the Valsalva manoeuvre during hyperthermia
Bibliographic record
Abstract
Abstract Background During hyperthermia, the perturbations in mean arterial blood pressure (MAP) produced by the Valsalva manoeuvre (VM) are more severe. However, whether these more severe VM‐induced changes in MAP are translated to the cerebral circulation during hyperthermia is unclear. Methods Healthy participants (n = 12, 1 female, mean ± SD: age 24 ± 3 years) completed a 30 mmHg (mouth pressure) VM for 15 s whilst supine during normothermia and mild hyperthermia. Hyperthermia was induced passively using a liquid conditioning garment with core temperature measured via ingested temperature sensor. Middle cerebral artery blood velocity (MCAv) and MAP were recorded continuously during and post‐VM. Tieck's autoregulatory index was calculated from the VM responses, with pulsatility index, an index of pulse velocity (pulse time) and mean MCAv (MCAvmean) also calculated. Results Passive heating significantly raised core temperature from baseline (37.9 ± 0.2 vs. 37.1 ± 0.1°C at rest, p < 0.01). MAP during phases I through III of the VM was lower during hyperthermia (interaction effect p < 0.01). Although an interaction effect was observed for MCAvmean (p = 0.02), post‐hoc differences indicated only phase IIa was lower during hyperthermia (55 ± 12 vs. 49.3 ± 8 cm s−1 for normothermia and hyperthermia, respectively, p = 0.03). Pulsatility index was increased 1‐min post‐VM in both conditions (0.71 ± 0.11 vs. 0.76 ± 0.11 for pre‐ and post‐VM during normothermia, respectively, p = 0.02, and 0.86 ± 0.11 vs. 0.99 ± 0.09 for hyperthermia p < 0.01), although for pulse time only main effects of time (p < 0.01), and condition (p < 0.01) were apparent. Conclusion These data indicate that the cerebrovascular response to the VM is largely unchanged by mild 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 | 0.000 |
| 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".