Retrospective Analysis of Esophageal Heat Transfer for Active Temperature Management in Post-cardiac Arrest, Refractory Fever, and Burn Patients
Bibliographic record
Abstract
Core temperature management is an important aspect of critical care; preventing unintentional hypothermia, reducing fever, and inducing therapeutic hypothermia when appropriate are each tied to positive health outcomes. The purpose of this study is to evaluate the performance of a new temperature management device that uses the esophageal environment to conduct heat transfer. De-identified patient data were aggregated from three clinical sites where an esophageal heat transfer device (EHTD) was used to provide temperature management. The device was evaluated against temperature management guidelines and best practice recommendations, including performance during induction, maintenance, and cessation of therapy. Across all active cooling protocols, the average time-to-target was 2.37 h and the average maintenance phase was 22.4 h. Patients spent 94.9% of the maintenance phase within ±1.0°C and 67.2% within ±0.5°C (574 and 407 measurements, respectively, out of 605 total). For warming protocols, all of the patient temperature readings remained above 36°C throughout the surgical procedure (average 4.66 h). The esophageal heat transfer device met performance expectations across a range of temperature management applications in intensive care and burn units. Patients met and maintained temperature goals without any reported adverse events.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".