High definition video teaching module for learning neck dissection
Bibliographic record
Abstract
INTRODUCTION: Video teaching modules are proven effective tools for enhancing student competencies and technical skills in the operating room. Integration into post-graduate surgical curricula, however, continues to pose a challenge in modern surgical education. To date, video teaching modules for neck dissection have yet to be described in the literature. PURPOSE: To develop and validate an HD video-based teaching module (HDVM) to help instruct post-graduate otolaryngology trainees in performing neck dissection. METHODS: This prospective study included 6 intermediate to senior otolaryngology residents. All consented subjects first performed a control selective neck dissection. Subjects were then exposed to the video teaching module. Following a washout period, a repeat procedure was performed. Recordings of the both sets of neck dissections were de-identified and reviewed by an independent evaluator and scored using the Observational Clinical Human Reliability Assessment (OCHRA) system. RESULTS: In total 91 surgical errors were made prior to the HDVM and 41 after exposure, representing a 55% decrease in error occurrence. The two groups were found to be significantly different. Similarly, 66 and 24 staff takeover events occurred pre and post HDVM exposure, respectively, representing a statistically significant 64% decrease. CONCLUSION: HDVM is a useful adjunct to classical surgical training. Residents performed significantly less errors following exposure to the HD-video module. Similarly, significantly less staff takeover events occurred following exposure to the HDVM.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".