The Use of Extracorporeal Membrane Oxygenation in Severely Burned Patients: A Survey of North American Burn Centers
Bibliographic record
Abstract
Respiratory failure and acute respiratory distress syndrome can occur in burn patients with or without inhalational injury and can significantly increase mortality. For patients with severe respiratory failure who fail conventional therapy with mechanical ventilation, the use of venovenous extracorporeal membrane oxygenation (ECMO) may be a life-saving salvage therapy. There have been a series of case reports detailing the use of ECMO in burn patients over the last 20 years, but very little is currently known about the status of ECMO use at burn centers in North America. Using a web-based survey of burn center directors in Canada and the United States, we examined the rate of usage of ECMO in burn care, barriers to its use, and the perioperative management of burn patients receiving ECMO therapy. Our findings indicate that approximately half of the burn centers have used ECMO in the care of burn patients, but patient volume is very low on average (less than 1 per year). Of centers that do use ECMO in burn care, only 40% have a specified protocol for doing so. Approximately half have operated on patients being actively treated with ECMO therapy, but perioperative management of anticoagulation varies widely. A lack of experience and institutional support and a perceived lack of evidence to support ECMO use in burn patients were the most commonly identified barriers to more widespread uptake. Better collaboration between burn centers will allow for the creation of consensus statements and protocols to improve outcomes for burn patients who require ECMO.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".