Surgical mesh information on YouTube(TM): Evaluating the usage and reliability of videos for patient education
Bibliographic record
Abstract
INTRODUCTION: Patients in search of answers to health-related questions often seek out information on the internet. The current study aimed to evaluate the quality of videos on the topic of mesh pertaining to its use in the treatment of stress urinary incontinence or pelvic organ prolapse. METHODS: were screened in this study. From that, a further 30 were selected for review. Five experts in the medical field reviewed each video anonymously, using two video assessment tools. Video characteristics were collected and evaluated. Videos were assessed based on a Global Assessment Score (GAS) and Patient Education Tool for Audiovisual Materials (PEMAT-A/V) scale for ease of patient access and comprehension. The overall correlation between raters and videos was also compared. RESULTS: The GAS and PEMAT-A/V ratings correlation across multiple raters demonstrated excellent inter-rater reliability. We found that the overall GAS score and recommendation was substandard, and the median PEMAT-A/V understandability score was 70% (poorly understandable). Most videos contained some form of marketing, and a scarce number had reliable sources of information. Evidence of neutrality was low. CONCLUSIONS: and the need for further education regarding patient resources.
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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 | 0.014 | 0.092 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".