Heat Transfer Capabilities of Surface Cooling Systems for Inducing Therapeutic Hypothermia
Bibliographic record
Abstract
Therapeutic hypothermia (TH) is used to treat patients with cerebral ischemia. Body surface cooling provides a simple noninvasive method to induce TH. We compared three surface cooling systems (Arctic Sun with adhesive ArcticGel pads [AS]); Blanketrol III with two nonadhesive Maxi-Therm Lite blankets [BL]); and Blanketrol III with nonadhesive Kool Kit [KK]). We hypothesized that KK would remove more heat due to its tighter fit and increased surface area. Eight subjects (four females) were cooled with each system set to 4°C outflow temperature for 120 minutes. Heat loss, skin and esophageal temperature, and metabolic heat production were measured. Skin temperature was higher with KK ( p = 0.002), heat loss was lower with KK in the first hour ( p = 0.014) but not after 120 minutes. Heat production increased similarly with all systems. Core temperature decrease was greater for AS (0.57°C) than BL (0.14°C; p = 0.035), but not KK (0.24°C; p = 0.1). Each system had its own benefits and limitations. Heat transfer capability is dependent on the cooling pump unit and the design of the liquid-perfused covers. Both Arctic Sun and Blanketrol III cooling/pump units had 4°C output temperatures. However, the Blanketrol III unit had a greater flow rate and therefore more cooling power. The nonadhesive BL and KK covers were easier to apply and remove compared with the adhesive AS pads. AS had an early transient advantage in heat removal, but this effect decreased over the course of cooling, thus minimizing or eliminating any advantage during longer periods of cooling that occur during clinical TH. Clinical Trial Registration number: NCT04332224.
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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 | 0.001 |
| 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".