Whole-body hypothermia has central and peripheral influences on elbow flexor performance
Bibliographic record
Abstract
The superimposed twitch technique was used to study the effect of whole-body hypothermia on maximal voluntary activation of elbow flexors. Seven subjects [26.4 ± 4 years old (mean ± SD)] were exposed to 60 min of either immersion in 8°C water (hypothermia) or sitting in 22°C air (control). Voluntary activation was assessed during brief (3 s) maximal voluntary contractions (MVCs) and then during a 2 min fatiguing sustained MVC. Hypothermia (core temperature 34.8 ± 0.9°C) decreased maximal voluntary torque from 98.2 ± 1.0 to 82.8 ± 5.8% MVC (P < 0.001) and increased central conduction time from 7.9 ± 0.4 to 9.1 ± 0.7 ms (P < 0.05). Hypothermia also decreased maximal resting twitch amplitude from 17.6 ± 4.0 to 10.0 ± 1.7% MVC (P < 0.005) and increased the time-to-peak twitch tension from 55.4 ± 4.0 to 79.0 ± 11.7 ms (P < 0.001). During the 2 min contraction, hypothermia decreased initial torque (P < 0.01) but attenuated the subsequent rate of torque decline (control from 95.5 ± 4 to 29.4 ± 8% MVC; and hypothermia from 85.3 ± 8 to 37.3 ± 5% MVC; P < 0.01). Cortical superimposed twitches increased as fatigue developed but were always lower in the hypothermic conditions. Cortical superimposed twitches increased from a value of 0.4 ± 0.3% MVC prefatigue to 3.9 ± 1.4% MVC postfatigue (P < 0.001) in the hypothermic conditions and from 1.7 ± 0.9 to 5.5 ± 2.3% MVC in control conditions. Our results suggest that hypothermia decreases MVCs primarily via peripheral mechanisms and attenuates the rate of fatigue development by reducing central 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 | 0.000 |
| 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".