Firefighters muscular recovery after a heavy work bout in the heat
Bibliographic record
Abstract
Occasionally firefighters need to perform very heavy bouts of work, such as smoke diving or clearing an accident site, which induce significant muscle fatigue. The time span for muscular recovery from such heavy work is not known. The purpose of this study was to evaluate firefighters' force-, neural-, metabolic-, and structural-related recovery after task-specific heavy work in the heat. Fifteen healthy firefighters (14 males and 1 female) performed a 20-min heavy work bout that simulated smoke diving and the clearance of an accident site at 35 °C. After the work, muscular recovery was evaluated by wrist flexion maximal voluntary contraction (MVC), average electromyography during MVC and during 10%MVC, rate of force production, motor response and stretch reflex responses, muscle oxygen consumption and oxygenation level, and wrist flexor muscle pennation angle. Recovery was followed for 4 h. Each of the 12 measured parameters changed significantly (p < 0.05) from those at baseline during the follow-up. Muscle oxygen consumption and the wrist flexor pennation angle remained elevated throughout the follow-up (oxygen consumption baseline, 12.9 ± 1.7 mL O2·min(-1)·(100 g)(-1); 4-h value, 17.5 ± 1.6 mL O2·min(-1)·(100 g)(-1); p < 0.05 and pennation angle baseline, 15.7 ± 0.8°; 4-h value, 17.8 ± 0.8°; p < 0.05). Muscle reoxygenation rate was elevated for up to 2 h (baseline, 2.3 ± 0.4 μmol·L(-1)·min(-1); 2-h value, 3.4 ± 0.4 μmol·L(-1)·min(-1); p < 0.05). The other 9 parameters recovered (were no longer significantly different from baseline) after 20 to 60 min. We concluded that the recovery order in main components of muscle function from fastest to slowest was force, neural, metabolic, and structural.
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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".