A Survey of Mechanical Ventilator Practices Across Burn Centers in North America
Bibliographic record
Abstract
Burn injury introduces unique clinical challenges that make it difficult to extrapolate mechanical ventilator (MV) practices designed for the management of general critical care patients to the burn population. We hypothesize that no consensus exists among North American burn centers with regard to optimal ventilator practices. The purpose of this study is to examine various MV practice patterns in the burn population and to identify potential opportunities for future research. A researcher designed, 24-item survey was sent electronically to 129 burn centers. The χ, Fisher's exact, and Cochran-Mantel-Haenszel tests were used to determine if there were significant differences in practice patterns. We analyzed 46 questionnaires for a 36% response rate. More than 95% of the burn centers reported greater than 100 annual admissions. Pressure support and volume assist control were the most common initial MV modes used with or without inhalation injury. In the setting of Berlin defined mild acute respiratory distress syndrome (ARDS), ARDSNet protocol and optimal positive end-expiratory pressure were the top ventilator choices, along with fluid restriction/diuresis as a nonventilator adjunct. For severe ARDS, airway pressure release ventilation and neuromuscular blockade were the most popular. The most frequently reported time frame for mechanical ventilation before tracheostomy was 2 weeks (25 of 45, 55%); however, all respondents reported in the affirmative that there are certain clinical situations where early tracheostomy is warranted. Wide variations in clinical practice exist among North American burn centers. No single ventilator mode or adjunct prevails in the management of burn patients regardless of pulmonary insult. Movement toward American Burn Association-supported, multicenter studies to determine best practices and guidelines for ventilator management in burn patients is prudent in light of these findings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".