Use of a Caprine Model for Simulation and Training of Endoscopic Ear surgery
Bibliographic record
Abstract
BACKGROUND: The objective of this study was to evaluate the utility of a caprine model in endoscopic ear surgical education using the index procedures of tympanoplasty and ossiculoplasty. Specifically, this study assessed the face and content validity of the caprine model, and the potential impact of anatomical differences on trainee understanding of human middle ear anatomy. METHODS: Twelve otolaryngology trainees attended a 3-hour endoscopic ear surgery course utilizing the caprine model in which they completed canalplasty, tympanoplasty, and ossiculoplasty. Prior to the course, the trainees completed a self-reported needs assessment and knowledge assessment of human middle ear anatomy. Following the course, the trainees repeated the knowledge assessment and completed evaluation and validation questionnaires. Five-point Likert scores were used for the needs assessment and validation questionnaire. RESULTS: Of the 12 trainees, 9 participated in the study. All domains of the learner needs assessment showed an average improvement of 1 point on the post-course evaluation with 6 of 9 domains being significantly improved using the Wilcoxon signed-rank test (P< .05). The model achieved validation in the domains of face, content, and global content validity with an average Likert score > 4. Knowledge assessment scores increased by 7% (P=.23) after the course compared to before. CONCLUSION: The caprine model offers an effective surgical simulation model for endoscopic ear surgery training with good face and content validity. We find it to be readily available and affordable. We currently use it routinely to give otolaryngology residents the experience of endoscopic ear surgery before operating on patients.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".