Is YouTube a Reliable Source of Information for Sacral Neuromodulation in Lower Urinary Tract Dysfunction?
Bibliographic record
Abstract
Background/Objectives: YouTube is an open-access video streaming platform with minimal regulation which has led to a vast library of unregulated medical videos. This study assesses the quality of information, understandability and actionability of videos on YouTube pertaining to sacral neuromodulation (SNM). Methods: The first 50 videos on YouTube after searching “sacral neuromodulation for bladder dysfunction” were reviewed. Thirty-eight of these videos met the inclusion criteria. These videos were reviewed by two Urology Registrars and the videos were scored using two standardised tools. The DISCERN tool assesses quality of information and the Patient Education Materials Assessment Tool for Audiovisual Material (PEMAT-A/V) tool assesses user understandability and accessibility. Results: Forty-two percent of videos were deemed to be poor or very poor, with 58% being fair, good or excellent according to the DISCERN standardised tool. For PEMAT-A/V the average score for understandability was 74% (43–100%) and actionability was 38% (0–100%). We found statistical significance comparing the duration of videos to the DISCERN groups (p = 0.02). We also found significance comparing the understandability of videos using the PEMAT-A/V score to the DISCERN groups (p ≤ 0.05). Conclusions: Forty-two percent of videos on SNM are of poor or very poor quality. The actionability score for consumers to seek out further information is also low at 38%. This raises concerns about the quality of information that is widely available on YouTube and how consumers will use this information when making decisions about their health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".