Esophageal Heat Transfer for Patient Temperature Control and Targeted Temperature Management
Bibliographic record
Abstract
Controlling patient temperature is important for a wide variety of clinical conditions. Cooling to normal or below normal body temperature is often performed for neuroprotection after ischemic insult (e.g. hemorrhagic stroke, subarachnoid hemorrhage, cardiac arrest, or other hypoxic injury). Cooling from febrile states treats fever and reduces the negative effects of hyperthermia on injured neurons. Patients are warmed in the operating room to prevent inadvertent perioperative hypothermia, which is known to cause increased blood loss, wound infections, and myocardial injury, while also prolonging recovery time. There are many reported approaches for temperature management, including improvised methods that repurpose standard supplies (e.g., ice, chilled saline, fans, blankets) but more sophisticated technologies designed for temperature management are typically more successful in delivering an optimized protocol. Over the last decade, advanced technologies have developed around two heat transfer methods: surface devices (water blankets, forced-air warmers) or intravascular devices (sterile catheters requiring vascular placement). Recently, a novel device became available that is placed in the esophagus, analogous to a standard orogastric tube, that provides efficient heat transfer through the patient's core. The device connects to existing heat exchange units to allow automatic patient temperature management via a servo mechanism, using patient temperature from standard temperature sensors (rectal, Foley, or other core temperature sensors) as the input variable. This approach eliminates vascular placement complications (deep venous thrombosis, central line associated bloodstream infection), reduces obstruction to patient access, and causes less shivering when compared to surface approaches. Published data have also shown a high degree of accuracy and maintenance of target temperature using the esophageal approach to temperature management. Therefore, the purpose of this method is to provide a low-risk alternative method for controlling patient temperature in critical care settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".