The Otolaryngology boot camp: A scoping review evaluating commonalities and appraisal for curriculum design and delivery
Bibliographic record
Abstract
OBJECTIVE: Surgical boot camps are becoming increasingly popular in Otolaryngology-Head and Neck Surgery (OHNS) residency programs. Despite pioneering virtual reality and simulation-based surgical education, these boot camps have lacked critical appraisal. The objective of this article was to examine the adoption and utility of surgical boot camps in OHNS residency training programs around the world. DATA SOURCES: Ovid Medline and PubMed databases were systematically searched in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for scoping reviews. Additionally, a grey literature search was performed. REVIEW METHODS: Inclusion criteria were peer-reviewed publications and grey literature sources that reported on OHNS boot camps for the novice learner. The search was restricted to human studies published in English. Studies were excluded if they were not examining junior trainees. RESULTS: A total of 551 articles were identified. Following removal of duplicates, screening, and full text review, 16 articles were included for analysis. Seven major boot camps were identified across various academic sites in the world. Most boot camps were one-day intensive camps incorporating a mixture of didactic, skill specific, and simulation sessions using an array of task trainers and high-fidelity simulators focusing on OHNS emergencies. Studies measuring trainee outcomes demonstrated improvement in trainee confidence, immediate knowledge, and skill acquisition. CONCLUSION: Surgical boot camps appear to be an effective tool for short term knowledge and skill acquisition. Further studies should examine retention of skill and maintenance of confidence over longer intervals, as little is known about these lasting effects.
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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 | 0.078 | 0.253 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.029 | 0.025 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".