Evaluating YouTube as a Source of Patient Information for Functional Endoscopic Sinus Surgery
Bibliographic record
Abstract
Objective: To evaluate the quality of information presented on YouTube regarding functional endoscopic sinus surgery (FESS) for patients. Methods: YouTube was searched using FESS-specific keywords under the setting of “relevance.” The first 50 videos from each keyword were reviewed and analyzed by 2 independent physician reviewers. Videos not related to FESS and duplicates were excluded. Outcome measures included the modified DISCERN score (range 0-5), the Journal of the American Medical Association ( JAMA) benchmark criteria (range: 0-4), a novel scoring checklist for FESS assessing usefulness (range: 0-16), and the Video Power Index (VPI). Intraclass correlation coefficient (ICC) was calculated. Results: Of the 200 videos identified, 95 videos were analyzed after exclusions. Videos had an average VPI of 40.8 and SD 133.2. Average scores from the 3 objective checklists among all videos were low: modified DISCERN: 1.91, SD: 1.15; JAMA benchmark: 1.91, SD: 0.76; and FESS score: 3.54, SD: 1.77. The ICC between the 2 independent reviewers was excellent for all 3 checklists. We noted significant positive Pearson correlation between all 3 checklist scores ( P < .001). In between-group comparisons of mean scores, there was significantly higher DISCERN and JAMA scores for videos from university/professional organizations, as compared to videos from medical advertising/for-profit companies and independent users. There were no significant differences in FESS scores noted between the 3 groups. Conclusion: There were overall low scores across the modified DISCERN, JAMA benchmark criteria, and FESS scoring checklists, reflecting the poor quality of YouTube videos as a source of patient information for FESS.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".