Evaluating quality, understandability, and actionability of YouTube content for gender affirming surgery
Bibliographic record
Abstract
INTRODUCTION: The purpose of this study is to evaluate YouTube content about metoidioplasty on completeness of perioperative information, actionability, understandability, degree of misinformation, quality, and presence of commercial bias. METHODS: A YouTube search for "Metoidioplasty" was conducted and the first 100 video results were watched by five independent reviewers. Videos in English, <30 minutes in length were included and videos primarily showing surgical footage were excluded. Videos were evaluated between January 2022 and June 2022. Each video was evaluated for presenter demographics, channel/video statistics, and whether it covered topics including anatomy, treatment options, outcomes, procedure risks, and misinformation, and whether it had a clickbait title. Calculated scores for validated DISCERN and patient education materials assessment tool (PEMAT) metrics were the primary outcome variables used to quantify quality, actionability, and understandability. For PEMAT, a cutoff of 75% was used to differentiate between "poor" vs. "good/sufficient." Multivariate and univariate logistic regressions were performed to assess correlations among primary outcome variables and other variables. RESULTS: Of the 79 videos analyzed, 24% (n=19) were of high quality; 99% (n=78) had poor understandability and 100% (n=79%) had poor actionability. Patients/consumers were the most common publisher type (n=71, 90%). CONCLUSIONS: This study demonstrates metoidioplasty content available on YouTube is not comprehensive and is of poor quality, and poor actionability and understandability, demonstrating a clear need for more relevant, accessible, comprehensible, and accurate content.
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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 | 0.010 | 0.068 |
| 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 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".