The effect of head and neck per-cooling on neuromuscular fatigue following exercise in the heat
Bibliographic record
Abstract
The effect of localised head and neck per-cooling on central and peripheral fatigue during high thermal strain was investigated. Fourteen participants cycled for 60 min at 50% peak oxygen uptake on 3 occasions: thermoneutral control (CON; 18 °C), hot (HOT; 35 °C), and HOT with head and neck cooling (HOTcooling). Maximal voluntary force (MVF) and central activation ratio (CAR) of the knee extensors were measured every 30 s during a sustained maximal voluntary contraction (MVC). Triplet peak force was measured following cycling, before and after the MVC. Rectal temperatures were higher in HOTcooling (39.2 ± 0.6 °C) and HOT (39.3 ± 0.5 °C) than CON (38.1 ± 0.3 °C; P < 0.05). Head and neck thermal sensation was similar in HOTcooling (4.2 ± 1.4) and CON (4.4 ± 0.9; P > 0.05) but lower than HOT (5.9 ± 1.5; P < 0.05). MVF and CAR were lower in HOT than CON throughout the MVC (P < 0.05). MVF and CAR were also lower in HOTcooling than CON at 5, 60, and 120 s, but similar at 30 and 90 s into the MVC (P > 0.05). Furthermore, they were greater in HOTcooling than HOT at 30 s, whilst triplet peak force was preserved in HOT after MVC. These results provide evidence that central fatigue following exercise in the heat is partially attenuated with head and neck cooling, which may be at the expense of greater peripheral fatigue. Novelty Central fatigue was greatest during hyperthermia. Head and neck cooling partially attenuated the greater central fatigue in the heat. Per-cooling led to more voluntary force production and more peripheral fatigue.
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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) | 0.000 | 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".