Effects of physical exertion and heat on cerebrovascular response in professional firefighters (1183.4)
Bibliographic record
Abstract
Firefighting is a physically demanding occupation and a number of factors can compromise the firefighter’s (FF) health during duty, including inadequate cerebral blood flow (CBF) regulation. We hypothesized that dynamic cerebral pressure‐flow relationships will be altered in FF in response to acute physical exertion in the heat. Seven firefighters (age=30.8 ± 10.3 yrs) performed 30 minutes of treadmill walking at 65% of heart rate maximum in full turn‐out gear in an environmental chamber at 39°C. Testing involved: 5 minutes squat‐stand maneuvers at 0.05 Hz performed before and after the 30 minute treadmill test. CBF velocity was monitored in the middle cerebral artery (MCAv) using transcranial Doppler, blood pressure (BP) was recorded continuously using finger plethysmography, and 3‐lead electrocardiography recorded heart rate. Transfer function analysis (TFA) was used to determine the autoregulatory metrics. The results showed that the driven TFA metrics were not significantly different (p<0.05) pre‐ to post‐exercise for coherence (0.991 ± 0.007 vs 0.966 ± 0.037), gain (0.62 ± 0.15 vs 0.582 ± 0.199 cm/s/mmHg) and phase (0.634 ± 0.20 vs 0.612 ± 0.32 Rad), and power spectral density for both MCAv (22954.1 ± 11271.2 vs 24958.1 ± 13909.4 cm/s2) and BP (56121.0 ± 16177.9 vs 62612.6 ± 23002.4 mmHg2). In conclusion, the cerebral pressure ‐ flow relationship in FF was unaltered pre‐ to post‐ moderate exercise in the heat.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".