Validity and Usefulness of YouTube Videos Related to Endoscopic Transsphenoidal Surgery for Patient Information
Bibliographic record
Abstract
Abstract Objectives This article evaluates the completeness and accuracy of YouTube videos related to endoscopic transsphenoidal surgery (ETS) as a source for patient information. Design YouTube was searched using relevant terms pertaining to ETS. Videos were evaluated independently by two physician reviewers experienced in ETS. Video demographics including uploader source along with validity scores based on predetermined checklists were captured. Setting Internet. Participants Not applicable. Main Outcome Measures A novel ETS scoring checklist, the modified DISCERN criteria, and Journal of the American Medical Association (JAMA) benchmark score were used to measure completeness and accuracy of videos. video power index (VPI) was calculated to reflect popularity. Intraclass correlation coefficient was calculated for rater agreement. Results Seventy-nine videos were included in final scoring and analysis. The ETS score, DISCERN, JAMA, and mean VPI across all included videos were 5.0 ± 2.7, 2.4 ± 0.83, 2.19 ± 0.62, and 8.92 ± 18.1, respectively. Based on the ETS score checklist, 31 (39%) of the videos were rated as poor, 30 (38%) were moderately useful, 17 (22%) were useful, and 1 (1%) was exceptional. There was a significant positive correlation between the ETS, DISCERN, and JAMA scores (p < 0.001), but no correlation with VPI and the validity scores. There were no significant differences comparing validity scores based on the uploader source. Conclusion YouTube videos related to ETS have limited usefulness and poor overall validity for patient information. Clinicians should direct patients to other validated sources of information and aim to improve the comprehensiveness of ETS-related videos.
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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 | 0.012 | 0.122 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".