Comparison of the Physiological and Perceptual Strain Indices in Firefighters During Real-Life Emergency Incidents
Bibliographic record
Abstract
Firefighting is an occupation during which individuals are exposed to excessive cardiovascular and thermoregulatory strain. Recently it has been documented that physiological strain (PhSI) can be effectively predicted with the perceptual strain index (PeSI) during simulated firefighting activities, however, it is unknown whether these relationships will transfer to real-life incidents. PURPOSE: To compare the PhSI and the PeSI responses during real-life emergency incidents in firefighters. METHODS: Twelve 24 hr shifts were monitored encompassing forty-one firefighters (Mean ± SE for Age = 42 ± 2 yrs and [[Unsupported Character - Codename ­]] = 52 ± 2 mL·kg-1·min-1) on active duty during the summer months in Toronto (28.3 ± 1°C and 53 ± 11% R.H). Core temperature (Tc) using radio telemetry and heart rate (HR) were logged continuously during each shift. Resting values of Tc and HR during sleep were obtained and along with corresponding peak values (averaged over 5 min) from each active call to calculate PhSI. The highest perceptual ratings of thermal comfort and perceived exertion were reported by subjects post-call and recorded for subsequent calculation of PeSI. Total active calls (n=38) were divided by type: medical (MED, n=18), FIRE (n=8), auto extrication (AutoX, n=3), Elevator (n=4), and Other (n=5). RESULTS: Across call types, PeSI underestimated PhSI during MED (1.7 ± 0.2 vs. 3.0 ± 0.2), Elevator (1.4 ± 0.5 vs. 2.9 ± 0.4) and Other call types (1.9 ± 0.4 vs. 2.9 ± 0.4), respectively, but not for FIRE (3.5 ± 0.3 vs. 2.9 ± 0.3) or AutoX (3.9 ± 0.5 vs. 4.3 ± 0.6). No significant differences were observed for PhSI between call types CONCLUSIONS: It can be concluded that PhSI is generally underestimated by PeSI, however, as the level of firefighter encapsulation is increased, such as with FIRE and AutoX call types, PeSI becomes a more reliable predictor of PhSI. Supported by the Workplace Safety and Insurance Board of Ontario and MITACS
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".