Assessment of the content and quality of YouTube videos related zygomatic implants: A content–quality analysis
Bibliographic record
Abstract
INTRODUCTION: This study aimed to evaluate the content and quality of YouTube videos of zygomatic implants. METHODS: According to Google Trends (2021), "zygomatic implant" was the most preferred keyword related to the topic. Therefore, in this study "zygomatic implant" was used as a keyword for the video search. Demographic characteristics such as the number of views, likes/dislikes, comments, video duration, number of days after upload, uploaders, and target audiences of the videos were evaluated. To evaluate the accuracy and content quality of videos (available from YouTube), the video information and quality index (VIQI) and global quality scale (GQS) were used. Statistical analyses were performed using the Kruskal-Wallis test, Mann-Whitney U test, chi-square test, Fisher's exact chi-square test, Yates continuity correction, and Spearman correlation analysis (p < 0.05). RESULTS: A total of 151 videos were searched; 90 met all inclusion criteria. According to the video content score, 78.9% of the videos were identified as low content, 20% as moderate, and 1.1% as high content. There was no statistical difference between the groups in video demographic characteristics (p > 0.001). Conversely, information flow, accuracy of information, video quality and precision, and total VIQI scores were statistically different between the groups. The moderate-content group had a higher GQS score than the low-content group (p < 0.001). The videos were mainly uploaded (40%) from hospitals and universities. Most videos were targeted toward professionals (46.75%). Low-content videos had higher ratings than the moderate- and high-content videos. CONCLUSIONS: Most YouTube videos on zygomatic implants showed low-content quality. This implies that YouTube is not a reliable source of information on zygomatic implants. Dentists, prosthodontists, and oral and maxillofacial surgeons should be aware of the content of video-sharing platforms and take responsibility for enriching video content.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".