The Use of Emergency Physicians to Deliver Anesthesia for Orthopaedic Surgery in Austere Environments
Bibliographic record
Abstract
BACKGROUND: Five billion people, primarily in low-income and middle-income countries, cannot access safe, affordable surgical and anesthesia care, particularly for orthopaedic trauma. The rate-limiting step for many orthopaedic surgical procedures performed in the developing world is the absence of safe anesthesia. Even surgical mission teams providing surgical care are limited by the availability of anesthesiologists. Emergency physicians, who are already knowledgeable in airway management and procedural sedation, may be able to help to fulfill the need for anesthetists in disaster relief and surgical missions. METHODS: Following the 2010 earthquake in Haiti, an emergency physician was trained using the Emergency Physician's General Anesthesia Syllabus (EP GAS) to perform duties similar to those of certified registered nurse anesthetists. The emergency physician then provided anesthesia during surgical mission trips with an orthopaedic team from February 2011 to March 2017, in Milot, Haiti. This is a descriptive overview of this training program and prospectively collected data on the cohort of patients whom the surgical mission teams treated in Haiti during that time frame. RESULTS: A single emergency physician anesthetist provided anesthesia for 71 of the 172 orthopaedic surgical cases, nearly doubling the number of cases that could be performed. This also allowed the anesthesiologists to focus on pediatric and more difficult cases. Both immediately after the surgical procedure and at 1 year, there were no serious adverse events for cases in which the emergency physician provided anesthesia. CONCLUSIONS: Given emergency physicians' baseline training in airway management and sedation, well-supervised and focused extra training under the vigilant supervision of a board-certified anesthesiologist may allow emergency physicians to be able to safely administer anesthesia. Using emergency physicians as anesthetists in this closely supervised setting could increase the number of surgical cases that can be performed in a disaster setting.
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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 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".