Simulation-based otolaryngology – head and neck surgery boot camp: ‘how I do it’
Bibliographic record
Abstract
BACKGROUND: In otolaryngology, surgical emergencies can occur at any time. An annual surgical training camp (or 'boot camp') offers junior residents from across North America the opportunity to learn and practice these skills in a safe environment. The goals of this study were to describe the set-up and execution of a simulation-based otolaryngology boot camp and to determine participants' confidence in performing routine and emergency on-call procedures in stressful situations before and after the boot camp. METHODS: There were three main components of the boot camp: task trainers, simulations and an interactive panel discussion. Surveys were given to participants before and after the boot camp, and their confidence in performing the different tasks was assessed via multiple t-tests. RESULTS: Participants comprised 22 residents from 12 different universities; 10 of these completed both boot camp surveys. Of the nine tasks, the residents reported a significant improvement in confidence levels for six, including surgical airway and orbital haematoma management. CONCLUSION: An otolaryngology boot camp gives residents the chance to learn and practice emergency skills before encountering the emergencies in everyday practice. Their confidence in multiple skillsets was significantly improved after the boot camp. Given the shift towards competency-based learning in medical training, this study has implications for all surgical and procedural specialties.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".