The appeal of ‘Do It Yourself’ orthodontic aligners: A YouTube analysis
Bibliographic record
Abstract
Abstract Objective: The present study aimed to determine how the popularity of ‘Do It Yourself’ (DIY) aligner videos available on YouTube relates to authorship, video content, quality and reliability, and to determine why DIY aligners appeal to consumers. Methods: The Google Trends website was interrogated to identify the most frequently used search terms regarding DIY aligners which were subsequently applied to a search of the YouTube website. One hundred twenty-three videos were assessed for completeness of content, reliability (using a modified version of the DISCERN tool) and quality using the Global Quality Score (GQS). The relationship between the variables and authorship, popularity, financial interest, and recommendations were assessed using Pearson Correlation Coefficients. Results: Laypeople produced the majority of the videos (73%). Dentists/Orthodontists uploaded only 4% of the videos, and dental professional bodies uploaded none. Most videos (86%) were content poor, unreliable (average DISCERN score of 1) and of low quality (average GQS of 2). The more popular, reliable and superior the quality of the video, the greater the number of views, likes and viewing rate ( p < 0.05). Conversely, authors with a financial interest and lower quality and less reliable videos were more likely to recommend DIY aligners. Consumers sought DIY aligner treatment due to a reduced cost. Conclusions: YouTube should not be considered as a viable nor reliable source of DIY aligner information for patients or the public. Dentists/Orthodontists should be encouraged to publish comprehensive and more informative YouTube content related to DIY aligners.
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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 | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 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".