Chemical Heat Packs as an Intervention to Prolong Ultrasound Battery Runtime
Bibliographic record
Abstract
INTRODUCTION: Point-of-care ultrasonography is a burgeoning field of practice and study. Although point-of-care ultrasonography has started to penetrate the field of wilderness medicine, its use in cold environments is often limited by poor battery performance. In the current study, we investigated the effect of chemical foot warmers on battery runtime of handheld ultrasound machines in cold weather. METHODS: This prospective study used a balanced, crossover randomization design to compare handheld ultrasound machines exposed to cold weather with and without the application of chemical foot warmers. Time to power off, ambient temperature, air humidity, and wind speed were recorded for each trial and compared in a mixed-effects model analysis of variance. RESULTS: Ultrasound machines showed significantly decreased functional battery life with decreasing temperature (P<0.01) and increasing wind speed (P=0.01). Addition of the chemical foot warmer resulted in a statistically significant increase in handheld ultrasound machine runtime compared with controls (P<0.001). Overall machine runtime was increased by approximately 21.8 min (95% CI 16.2-27.3). CONCLUSIONS: The application of chemical foot warmers to handheld ultrasound machines exposed to cold environments prolongs usable battery life and, consequently, may allow for extended scanning time in austere and remote environments.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".