Simulation of Urgent Airway Management in a Postthyroidectomy Hematoma
Bibliographic record
Abstract
Introduction: An expanding neck hematoma following thyroidectomy is a rare complication requiring urgent airway management and potential bedside evacuation before definitive surgical intervention. Due to its rare occurrence and life-threatening consequences, appropriate crisis resource management and a systematic approach are critical for patient safety. Methods: In this simulation scenario using a high-fidelity mannequin, a 68-year-old male presented with an expanding cervical hematoma 2 hours after a total thyroidectomy. The target audience was junior residents (PGY 1, PGY 2) in otolaryngology-head and neck surgery. Residents were given a case stem to encourage active information gathering through history and physical examination. Setup and flow of the scenario were designed for residents to prioritize establishing an airway through bedside decompression of the hematoma prior to making operating room arrangements for definitive management. Standardized patients playing a ward nurse and patient family member added complexity to the case. Results: Since 2012, the simulation has been used with a total of 96 residents as part of an annual boot camp. Surveys conducted after the boot camp verified the effectiveness of simulations in learning and, specifically, the usefulness of this scenario. Discussion: Simulation-based training is an effective learning modality for critical cases in health care disciplines involving emergency airway management. A well-developed simulation that closely resembles a real-life scenario is essential in creating a rich learning environment for trainees. Our scenario can be a valuable resource for other institutions implementing simulation-based training as part of their medical education.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".