Quality and interaction levels of Instagram posts related to orthodontic #clearaligners
Bibliographic record
Abstract
Introduction: Developing technology and increased utilization of Internet have raised awareness and facilitated access to information about health-related issues.Objectives: This study aimed to evaluate the content and quality of Instagram posts about orthodontic clear aligners. Material and methods:A search using the hashtag #clearaligners was performed in the Instagram application.After pre-evaluation of the top 250 posts, those that did not meet the evaluation criteria were excluded, and the remaining 60 posts were analyzed.To determine quality of the posts, a quality score was created to evaluate content accuracy, consistency between the image and text, and image quality.Each post was assessed and given a quality score ranging from 0 to 20.According to this score, posts were classified as of poor, moderate, or excellent quality.Descriptive statistics, Kruskal-Wallis H test and Pearson's correlation coefficients were calculated for statistical evaluations.Results: The 60 posts analyzed had a mean quality score of 14 ± 3, indicating moderate quality.Posts from dentists/ orthodontists and laypersons presented comparable quality scores.Number of likes and number of comments were positively correlated, but these parameters were not associated with quality score.When number of likes, number of comments, and quality score were compared according to the post's source and purpose, no significant differences were detected between the groups (p > 0.05).Conclusions: Our study indicates that Instagram posts about clear aligners are of moderate quality.We believe that dentists/ orthodontists in particular should be more aware about sharing information in order to contribute to reliability and quality of medical content of popular social media platforms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".