Successful Use of Extracorporeal Membrane Oxygenation Therapy in Patients With 80% Full Thickness Burns
Bibliographic record
Abstract
Despite advances in burn care, mortality in adult patients with extensive burn injuries remains a concern, particularly in those who develop concurrent acute respiratory distress syndrome (ARDS). In cases of ARDS refractory to conventional treatments, venovenous extracorporeal membrane oxygenation (ECMO) may represent a viable salvage therapy, even in the major burn population. We present the case of a 38-year-old man with full thickness burns to over 80% of his body, who developed severe ARDS 4 days postburn. After failing to respond to deep sedation, paralysis, and proning, ECMO therapy was initiated to maintain oxygenation and ventilation. Over the next 14 days, while the patient was treated with ECMO, he successfully underwent three major operations to debride and allograft approximately 65% of his body surface area, including one in the prone position. ECMO therapy was discontinued on postburn day 18, and the patient had his wounds reconstructed and survived his injuries. To the best of our knowledge, this is the first report of a burn patient with such severe burns requiring surgical intervention that has been treated with ECMO and survived, and the first case of a burn patient on ECMO having surgery in the prone position. They conclude this case serves as a "proof of concept" that ECMO is a potential treatment for appropriately selected major burn patients with ARDS who fail to respond to other therapies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".