Blood Pressure Responses During A Firefighting Simulation On A Treadmill In A Thermoneutral Environment
Bibliographic record
Abstract
Firefighting activities result in high cardiovascular strain. Heart rate and electrocardiogram have been used during firefighting tasks in different studies to quantify their impact on cardiac strain. To our knowledge, no other parameter has been used to quantify cardiovascular strain during firefighting tasks. PURPOSE: To characterize blood pressure responses during a firefighting simulation and compare them to the maximal values reached in an incremental test. METHODS: To characterize blood pressure responses during a firefighting simulation and compare them to the maximal values reached in an incremental test. RESULTS: Thirty (30) healthy adults (3 females) and 11 firefighters (2 females) for a total of 41 participants (age: 27 ± 6 years) completed both tests. Mean heart rate, systolic and diastolic blood pressure and rate-pressure product during the firefighting simulation were respectively 138 ± 18 bpm, 167 ± 18 and 73 ± 9 mmHg and 22900 ± 4122 bpm·mmHg. No significative difference (p = 0.65) was observed in peak systolic blood pressure between the firefighting simulation (208 ± 27 mmHg) and the incremental test (208 ± 22 mmHg), but peak diastolic blood pressure was higher during the firefighting simulation (FS: 91 ± 11 mmHg, IT: 83 ± 14 mmHg, p < 0.01). Peak heart rate (FS: 170 ± 15, IT: 190 ± 9 bpm, p < 0.01) and rate-pressure product (FS: 32281 ± 5344, IT: 39289 ± 4689 bpm·mmHg, p < 0.01) were higher during the incremental test. CONCLUSIONS: Firefighting activities entail exaggerated blood pressure responses. These results highlight the high level of cardiovascular strain experienced by firefighters. It would seem imperative to quantify rate-pressure product in a prolonged firefighting intervention, a situation where body temperature rises even higher since heat stress and dehydration have a synergistic effect on increasing heart rate.
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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".