Bibliographic record
Abstract
With the dawn of the Internet era, information has become readily available and accessible to people worldwide. Over 70% of adults search the Internet for healthcare-related information. Among the most popular platforms is YouTube, a free video sharing platform that has quickly become one of the most widely used sources of online information, with over 2 billion video views daily and over 30 million subscribers. A 2018 Health Information National Trends Survey reported that more than 33% of patients watched health-related videos on YouTube. Dermal fillers are medical device implants approved for various cosmetic concerns such as moderate to severe facial rhytids, augmentation of facial features, lipoatrophy, and correction of contour deficiencies. According to the 2020 Plastic Surgery Statistics study, dermal fillers ranked as the second most common cosmetic procedure, after botulinum toxin A injections. The popularity of dermal fillers continue to rise due to general societal acceptance, their non‑invasive nature, and the increased availability of biocompatible and durable materials. The latter facilitates immediate and predictable results with minimal downtime. Education about fillers may involve a visual and auditory component to better help patients contextualize the process and set appropriate expectations. Particularly for first‑time filler patients, YouTube may be a first line resource. While online videos can be a valuable educational tool, information on the Internet is not always accurate and may originate from unreliable sources. Numerous studies have evaluated the accuracy and content of YouTube videos related to patient information. Studies have also investigated the quality of videos regarding botulinum toxin A injections. However, there is a lack of literature regarding filler content on this platform. This study aims to objectively assess the accuracy, quality, and completeness of YouTube videos on dermal fillers. The findings will help inform dermatologists and other injectors regarding the utility of YouTube as a tool for patient education.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".