The esophageal cooling device: A new temperature control tool in the intensivist's arsenal
Bibliographic record
Abstract
BACKGROUND: Therapeutic hypothermia has been demonstrated to improve neurological outcome in comatose survivors of cardiac arrest. Current temperature control modalities however, have several limitations. Exploring innovative methods of temperature management has become a necessity. METHODS: We describe the first use of a novel esophageal cooling device as a sole modality for hypothermia induction, maintenance and rewarming in a series of four postcardiac arrest patients. The device was inserted in a manner similar to standard orogastric tubes and connected to an external heat exchange unit. RESULTS: A mean cooling rate of 0.42 °C/hr (SD ± 0.26) was observed. An average of 4 hr 24 min (SD ± 2 hr 6 min) was required to reach target temperature, and this was maintained 90.25% (SD ± 16.20%) of the hypothermia protocol duration. No adverse events related to device use were encountered. Questionnaires administered to ICU nursing staff regarding ease-of-use of the device and its performance were rated as favorable. CONCLUSIONS: When used as a sole modality, objective performance parameters of the esophageal-cooling device were found to be comparable to standard temperature control methods. More research is required to further quantify efficacy, safety, assess utility in other patient populations, and examine patient outcomes with device use in comparison to standard temperature control modalities.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".