Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: To improve heat transfer, the Medivance Arctic Sun Temperature Management System (Medivance, Inc., Louisville, CO, USA) features an adhesive, water-conditioned, highly conductive hydrogel pad for intimate skin contact. This study measured and compared the heat transfer coefficient (h), i.e. heat transfer efficiency, of this pad (hPAD), in a heated model and in nine volunteers' thighs; and of 10 degrees C water (hWATER) in 33 head-out immersions by 11 volunteers. METHODS: Volunteer studies had ethical approval and written informed consent. Calibrated heat flux transducers measured heat flux (W m-2). Temperature gradient (DeltaT) was measured between skin and pad or water temperatures. Temperature gradient was changed through the pad's water temperature controller or by skin cooling on immersion. RESULTS: The heat transfer coefficient is the slope of W m-2/DeltaT: its unit is W m-2 degrees C-1. Average with (95% CI) was: model, hPAD = 110.4 (107.8-113.1), R2 = 0.99, n = 45; volunteers, hPAD = 109.8 (95.5-124.1), R2 = 0.83, n = 51; and water immersion, hWATER = 107.1 (98.1-116), R2 = 0.86, n = 94. CONCLUSION: The heat transfer coefficient for the pad was the same in the model and volunteers, and equivalent to hWATER. Therefore, for the same DeltaT and heat transfer area, the Arctic Sun's heat transfer rate would equal water immersion. This has important implications for body cooling/rewarming rates.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.022 |
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".