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 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 | 0.017 | 0.148 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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 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".