YouTube Information about Diabetes and Oral Healthcare (Preprint)
Bibliographic record
Abstract
BACKGROUND Diabetes mellitus is a chronic disease that is increasing at an alarming rate all over the world OBJECTIVE The aim of this study was to assess the information available on YouTube on diabetes and oral healthcare METHODS This cross-sectional study made a search in YouTube (https://www.youtube.com/) applying the search terms “oral healthcare for diabetics.” Two reviewers assessed the videos and categorized them into useful, misleading or personal experience, and scored them using a global quality scale (GQS) from 1-5 according to their overall quality (1 = poor quality; 5 = excellent quality). The source of each video was also registered, as was user interaction with each video RESULTS A total of 97 videos were included for analysis. Of these, 30 (30.9%) contained useful information, 61 (62.9%) contained misleading information, and six (6.2%) recounted personal experiences. Overall quality scores showed statistically significant differences between those containing useful information and those with misleading information and personal experience (p=0.001). Significant differences in content were also found regarding oral hygiene (p=0.022), periodontitis (p=0.002), and infection (p=0.04). CONCLUSIONS YouTube provides informative videos about oral healthcare for diabetics. The quality of the videos was variable and the videos recorded by dental professionals and Universities showed a higher quality. Further research is needed into oral healthcare for diabetics.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.325 | 0.109 |
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".