Is Cognitive Function Impaired while Working in a Climate Chamber at 30°C in Fire Fighters?
Bibliographic record
Abstract
Firefighting requires cognitive abilities, such as attention, vigilance, spatial awareness, decision making, and air management, under extreme life-threatening working conditions. To date, cognitive function during exertional heat stress in firefighters has been conducted utilizing simple mental performance tasks, such as reaction time, or lacking cognitive assessments while core temperature was increasing to detect any changes. PURPOSE: The purpose of this study was to examine the effects of thermal stress on various aspects of cognitive function during moderate-intensity treadmill walking. METHODS: Nineteen fire fighters were tested (age 35.6 ± 8.7 years, BMI 27.3 ± 3.2 kg.m-1, body fat 16.7 ± 5.8 %, VO2peak = 45.2 ± 5.5 mL.kg-1.min-1). Core temperature, skin temperature, and heart rate were continuously monitored and 5 mL.kg-1 of water was provided throughout the protocol. Firefighters walked on a motorized treadmill at 4.5 km.h-1 and 2.5% grade, in a climate chamber controlled at 30 °C and 50% relative humidity for 84.3 ± 13.4 min. Cognitive function was tested using the CANTABeclipse battery (spatial working memory - SWM, reaction time - RTI, rapid visual information processing - RVP, spatial span - SSP, and paired associates learning - PAL) at baseline, immediately following completion of exercise, and after attaining a hand and forearm immersion recovery core temperature of 37.8 °C, while SSP and PAL were also evaluated during exercise when core temperatures of 37.8, 38.5, and 39.0 °C were attained. RESULTS: Compared to baseline, post-tests for RVP revealed improvements in correct rejections (262.9 ± 8.6 vs 267.2 ± 5.9 rejections; p < 0.05), latency (380.3 ± 83.7 vs 334.9 ± 42.2 ms; p < 0.05), and simple reaction time (286.8 ± 35.1 vs 270.0 ± 31.2 ms; p < 0.05), while performance on the PAL test showed significantly more errors during the final level (5.7 ± 5.5 vs 9.6 ± 8.7 errors; p < 0.05) when core temperature reached 38.5°C. CONCLUSIONS: Taken together, it appears that visual memory and new learning is impaired when core temperature reaches 38.5°C without decrements in spatial information, sustained attention, or working memory capacity when the average rate of increase in core temperature is 0.87 ± 0.24 °C.h-1. This research was supported by the Workers Compensation Board of Manitoba.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".