Entering the Misinformation Age: Quality and Reliability of YouTube for Patient Information on Liposuction
Bibliographic record
Abstract
Background: YouTube is currently the most popular online platform and is increasingly being utilized by patients as a resource on aesthetic surgery. Yet, its content is largely unregulated and this may result in dissemination of unreliable and inaccurate information. The objective of this study was to evaluate the quality and reliability of YouTube liposuction content available to potential patients. Methods: YouTube was screened using the keywords: “liposuction,” “lipoplasty,” and “body sculpting.” The top 50 results for each term were screened for relevance. Videos which met the inclusion criteria were scored using the Global Quality Score (GQS) for educational value and the Journal of the American Medical Association (JAMA) criteria for video reliability. Educational value, reliability, video views, likes, dislikes, duration and publishing date were compared between authorship groups, high/low reliability, and high/low educational value. Results: A total of 150 videos were screened, of which 89 videos met the inclusion criteria. Overall, the videos had low reliability (mean JAMA score = 2.78, SD = 1.15) and low educational value (mean GQS score = 3.55, SD = 1.31). Videos uploaded by physicians accounted for 83.1% percent of included videos and had a higher mean educational value and reliability score than those by patients. Video views, likes, dislikes, comments, popularity, and length were significantly greater in videos with high reliability. Conclusions: To ensure liposuction-seeking patients are appropriately educated and informed, surgeons and their patients may benefit from an analysis of educational quality and reliability of such online content. Surgeons may wish to discuss online sources of information with patients.
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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 | 0.147 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".