Prolonged physical inactivity leads to a drop in toe skin temperature during local cold stress
Bibliographic record
Abstract
The purpose was to examine the effects of a prolonged period of recumbency on the toe temperature responses during cold-water foot immersion. Ten healthy males underwent 35 days of horizontal bed rest. The right foot of the subjects was assigned as the experimental (EXP) foot. To prevent bed rest-induced vascular deconditioning in the left control foot (CON), a sub-atmospheric vascular pressure countermeasure regimen was applied on the left lower leg for 4 × 10 min every second day. On the first (BR-1) and the last (BR-35) day of the bed rest, subjects performed two 30 min foot immersion tests in 8 °C water, one with the EXP foot and the other with the CON foot. The tests were conducted in counter-balanced order and separated by at least a 15 min interval. At BR-35, the average skin temperature of the EXP foot was lower than at BR-1 (-0.8 °C; P = 0.05), a drop that was especially pronounced in the big toe (-1.6 °C; P = 0.05). In the CON foot, the average skin temperature decreased by 0.6 °C in BR-35, albeit the reduction was not statistically significant (P = 0.16). Moreover, the pressure countermeasure regimen ameliorated immersion-induced thermal discomfort for the CON foot (P = 0.05). Present findings suggest that severe physical inactivity exaggerates the drop in toe skin temperature during local cold stress, and thus might constitute a potential risk factor for local cold injury.
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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".