Changes in manual dexterity following short-term hand and forearm immersion in 10 degrees C water.
Bibliographic record
Abstract
BACKGROUND: Following accidental immersion in cold water, the chance of survival through the initial short-term period may depend on manual dexterity for survival tasks. We aimed to study the time course of impairment of manual dexterity during the initial stages of immersion. METHODS: We investigated gross (manipulating harness buckles) and fine (Purdue Pegboard Assembly) manual dexterity of 11 male and 15 female subjects with either no immersion (Control) or after immersion of right and left hand and forearm in 10 degrees C water for 30, 120, and 300 s. Exposure lengths were presented in a counter-balanced order in a single session. RESULTS: Mean local skin temperature decreased significantly with cold-water immersion, with the amount of cooling proportional to the length of exposure. Buckle test times increased significantly from 9.1 +/- 3.0 s during Control to similar values of 19.5 +/- 11.1 s and 18.14 +/- 12.1 s after 120 and 300 s of immersion. Pegboard scores were significantly lower at 40.2 +/- 7.6 pieces following 300 s of immersion compared with Control and 30 s values of 49.0 +/- 6.4 and 47.2 +/- 6.9 pieces, respectively. CONCLUSIONS: It is concluded that fine and gross manual dexterity were rapidly and progressively impaired with short-term cold-water immersion. Therefore, efforts must be made to protect the hands/forearms from cold and to ensure survival equipment manipulation requires as little dexterity as possible.
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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) | 0.002 | 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".