Finger dexterity, skin temperature, and blood flow during auxiliary heating in the cold
Bibliographic record
Abstract
The primary purpose of the present study was to compare the effectiveness of two forms of hand heating and to discuss specific trends that relate finger dexterity performance to variables such as finger skin temperature (T(fing)), finger blood flow (Q(fing)), forearm skin temperature (T(fsk)), forearm muscle temperature (Tfmus), mean weighted body skin temperature (Tsk), and change in body heat content (DeltaH(b)). These variables along with rate of body heat storage, toe skin temperature, and change in rectal temperature were measured during direct and indirect hand heating. Direct hand heating involved the use of electrically heated gloves to keep the fingers warm (heated gloves condition), whereas indirect hand heating involved warming the fingers indirectly by actively heating the torso with an electrically heated vest (heated vest condition). Seven men (age 35.6 +/- 5.6 yr) were subjected to each method of hand heating while they sat in a chair for 3 h during exposure to -25 degrees C air. Q(fing) was significantly (P < 0.05) higher during the heated vest condition compared with the heated gloves condition (234 +/- 28 and 33 +/- 4 perfusion units, respectively), despite a similar T(fing) (which ranged between 28 and 35 degrees C during the 3-h exposure). Despite the difference in Q(fing), there was no significant difference in finger dexterity performance. Therefore, finger dexterity can be maintained with direct hand heating despite a low Q(fing). DeltaH(b), Tsk, and T(fmus) reached a low of -472 +/- 18 kJ, 28.5 +/- 0.3 degrees C, and 29.8 +/- 0.5 degrees C, respectively, during the heated gloves condition, but the values were not low enough to affect finger dexterity.
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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) | 0.001 | 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".