Trainability of cold induced vasodilatation in fingers and toes
Bibliographic record
Abstract
Subjects that repeatedly have to expose the extremities to cold may benefit from a high peripheral temperature to maintain dexterity and tissue integrity. Therefore, we investigated if repeated immersions of a hand and a foot in cold water resulted in increased skin temperatures. Nine male and seven female subjects (mean 20.4; SD 2.2 years) immersed their right (trained) hand and foot simultaneously in 8°C water, 30 min daily for 15 days. During the pre and post-test (days 1 and 15, respectively) the left (untrained) hand and foot were immersed as well. Pain, tactile sensitivity and skin temperatures were measured every day. Mean (SD) toe temperature of the trained foot increased from 9.49°C (0.89) to 10.03°C (1.38) (p < 0.05). The trained hand, however, showed a drop in mean finger temperature from 9.28°C (0.54) to 8.91°C (0.44) (p < 0.001) and the number of cold induced vasodilation (CIVD) reactions decreased from 52% during the first test to 24% during the last test. No significant differences occurred in the untrained extremities. Pain diminished over time and tactile sensitivity decreased with skin temperature. The combination of less CIVD responses in the fingers after training, reduced finger skin temperatures in subjects that did show CIVD and the reduced pain and tactile sensitivity over time may lead to an increased risk for finger cold injuries. It is concluded that repeated cold exposure of the fingers does not lead to favorable adaptations, but may instead increase the injury risk.
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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".