Influence of body heat content on hand function during prolonged cold exposures
Bibliographic record
Abstract
We examined the influence of 1) prior increase [preheating (PHT)], 2) increase throughout [heating (HT)], and 3) no increase [control (Con)] of body heat content (H(b)) on neuromuscular function and manual dexterity of the hands during a 130-min exposure to -20 degrees C (coldEx). Ten volunteers randomly underwent three passive coldEx, incorporating a 10-min moderate-exercise period at the 65th min while wearing a liquid conditioning garment (LCG) and military arctic clothing. In PHT, 50 degrees C water was circulated in the LCG before coldEx until core temperature was increased by 0.5 degrees C. In HT, participants regulated the inlet LCG water temperature throughout coldEx to subjective comfort, while the LCG was not operating in Con. Thermal comfort, rectal temperature, mean skin temperature, mean finger temperature (T(fing)), change in H(b) (DeltaH(b)), rate of body heat storage, Purdue pegboard test, finger tapping, handgrip, maximum voluntary contraction, and evoked twitch force of the first dorsal interosseus muscle were recorded. Results demonstrated that, unlike in HT and PHT, thermal comfort, rectal temperature, mean skin temperature, twitch force, maximum voluntary contraction, and finger tapping declined significantly in Con. In contrast, T(fing) and Purdue pegboard test remained constant only in HT. Generalized estimating equations demonstrated that DeltaH(b) and T(fing) were associated over time with hand function, whereas no significant association was detected for rate of body heat storage. It is concluded that increasing H(b) not only throughout but also before a coldEx is effective in maintaining hand function. In addition, we found that the best indicator of hand function is DeltaH(b) followed by T(fing).
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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".