Novel High‐Fidelity Peritonsillar Abscess Simulator
Bibliographic record
Abstract
OBJECTIVE: To create and assess a novel high-fidelity peritonsillar abscess simulation task trainer with junior otolaryngology-head and neck surgery residents. STUDY DESIGN: Prospective cohort study. SETTING: Third annual Emergencies in Otolaryngology Head & Neck Surgery Bootcamp course at the Canadian Surgical Technologies & Advanced Robotics in London, Canada. SUBJECTS AND METHODS: Fresh cadaveric material was obtained consisting of a head and neck. Abscess pockets were simulated with a finger of a latex glove containing vanilla pudding to represent pus, tied off with a silk suture. These abscess pockets were inserted into the peritonsillar space from the transected neck inferiorly, passing medial to the great vessels into the parapharyngeal space. Faculty members evaluated the models to test content validity. The primary outcome measure was a postbootcamp survey evaluation assessing specific domains: learning objectives, effectiveness of faculty, and the quality and realism of models. RESULTS: When working with this model, learners were able to locate, visualize, and manipulate the abscess. The materials and positioning of the pockets created high-fidelity models that were realistic in appearance and haptics feedback. CONCLUSIONS: A novel high-fidelity task trainer has been successfully developed to teach the technique of peritonsillar abscess incision and drainage. This task trainer is currently the highest-fidelity model reported in the literature. This model allows learners to practice a high-stakes emergency skill in a controlled environment, affording the opportunity to practice localization, visualization, and drainage of the abscess with a high level of realism.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".