The Usefulness of YouTube Videos Related to Endoscopic Sinus Surgery for Surgical Residents
Bibliographic record
Abstract
Abstract Objective The use of online teaching modalities to supplement surgical learning has increased recently, demonstrating promising results. Previous studies have analyzed the value and usefulness of YouTube as an educational source to learners, including teaching surgical skills to Otolaryngology–Head and Neck Surgery (OHNS) trainees. YouTube videos on endoscopic sinus surgery (ESS) still need to be explored as ESS remains a common, yet challenging surgery that OHNS residents encounter regularly. This study aimed to objectively evaluate the usefulness of YouTube videos on ESS for surgical education. Design YouTube was searched using the following keywords: “uncinectomy,” “maxillary antrostomy,” “anterior ethmoidectomy,” and “ethmoid bulla resection.” These represent the initial ESS steps residents learn. Each video was assessed for eligibility by two independent reviewers. Outcome Measures The LAParoscopic surgery Video Educational Guidelines (LAP-VEGaS) and ESS-specific criteria were used to assess educational quality. Video popularity index (VPI) was used to calculate video popularity. Results Of the 38 videos that met inclusion criteria, the average LAP-VEGaS score was 6.59 (± ) 3.23 standard deviation. Most videos were designated low quality. There was a weak positive correlation between whether a video included ESS-specific criteria and LAP-VEGaS score (r = 0.269, p = 0.102). There was a significant positive correlation between VPI and LAP-VEGaS scores (r = 0.497, p = 0.003). Conclusion Overall, the quality of included videos was poor. OHNS residents should not rely solely or primarily on YouTube videos to learn surgical skills relevant to ESS. To maximize potential of online teaching, high-quality videos should be used to compliment other methods of teaching.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".