Are YouTube videos a reliable information source for young women with metastatic breast cancer?
Bibliographic record
Abstract
244 Background: Young women with metastatic breast cancer (MBC) are an underserved population, rendering them susceptible to media sources that lack in credibility and reliability. YouTube (YT) is the most commonly used search engine following Google. This study aims to assess the quality of MBC YT videos and to identify common themes in MBC experiences. Methods: A systematic review of YT videos with search terms “metastatic breast cancer young” was conducted in 08/2021. Title, date uploaded, length, poster identity, number of likes, dislikes, and comments were collected. Understandability, actionability were assessed using the Patient Education Materials Assessment Tool (PEMAT) for audiovisual (A/V) materials; information reliability/quality was assessed with DISCERN. Scoring was done by 3 reviewers. Themes, presence of sponsorships, healthcare professionals’ and patients’ narratives were also reported. Results: 101 videos were identified. Of these, 78.2% were sponsored. Average video length was 14.9 minutes (SD 22.5). Majority were posted by nonprofit groups and breast cancer advocacy organizations. Mean PEMAT A/V score was 78.8% (SD 15.3) and 43.1% (SD 45.2) for understandability and actionability, respectively. Overall, videos had moderate reliability and quality levels; mean DISCERN score was 2.44/5 (SD 0.7). Patient narratives were shared in 63.3% and healthcare professionals in 57.4%. Identified themes include treatment (66.3%), family relationship (45.5%), motherhood (37.6%), terminal status (31.6%), the path to diagnosis (28.7%), and spousal relationship (24.7%). Conclusions: YouTube videos about MBC are highly understandable but demonstrate low to moderate rates of actionability, with low reliability and quality scores. Many have a potential commercial bias. More research is needed to evaluate their impact on patient decisions and possible interventions provided by healthcare institutions.[Table: see text]
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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 | 0.016 | 0.150 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".