What Determines the Efficacy of Forced-Air Warming Systems? A Manikin Evaluation with Upper Body Blankets
Bibliographic record
Abstract
BACKGROUND: Forced-air warming has gained acceptance as an effective means to prevent perioperative hypothermia. However, little is known about the influence of air flow and air temperature at the nozzle and the influence of heat distribution in the blankets on the efficacy of these systems. METHODS: We conducted a manikin study with heat flux transducers using five forced-air warming systems to determine the factors that are responsible for heat transfer from the blanket to the manikin. RESULTS: There was no relation between air temperature at the nozzle of the power unit and the resulting heat transfer. There was also no relation between the air flow at the nozzle of the power unit and the resulting heat transfer. However, all blankets performed best at high air flows above 19 L/s. The heat exchange coefficient, the mean temperature gradient between the blanket and the manikin correlated positively with the resulting heat transfer and the difference between the minimal and maximal blanket temperature correlated negatively with the resulting heat transfer. CONCLUSIONS: The efficacy of forced-air warming systems is primarily determined by the blanket. Modern power units provide sufficient heat energy to maximize the ability of the blanket to warm the patient. Optimizing blanket design by optimizing the mean temperature gradient between the blanket and the manikin (or any other surface) with a very homogeneous temperature distribution in the blanket will enable the manufacturers to develop better forced-air warming systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".