Misinformation and Readability of Social Media Content on Pediatric Ankyloglossia and Other Oral Ties
Bibliographic record
Abstract
Importance: Diagnosis of pediatric ankyloglossia and other oral ties is increasing in part due to social media, leading to more frenotomies and excess medicalization of often normal anatomy. Objective: To assess the accuracy and readability of social media content on pediatric ankyloglossia and other oral ties. Design, Setting, and Participants: In this cross-sectional study, the top 200 posts on an image-based social media platform tagged with #tonguetie, #liptie, or #buccaltie were collected using a de novo account on March 27, 2023. Post metadata and caption and content text were extracted. Main Outcomes and Measures: Misinformation was judged by a 30-point scoring sheet based on clinical practice guidelines and expert consensus that was developed by 3 fellowship-trained pediatric otolaryngologist-head and neck surgeons. Readability was assessed using the Flesch-Kincaid Grade Level, Flesch Reading Ease, and Simple Measure of Gobbledygook scales. Quality was scored using the JAMA Benchmark Criteria. Results: After removing duplicates and irrelevant content, 71 unique posts from 68 unique accounts were included in the analysis. Business and practice accounts made up most of the account types (60 [84.5%]) compared with individual and personal accounts (11 [15.5%]). Most accounts (49 [69.0%]) were run by individuals who self-identified as health care practitioners, and 21 posts (29.6%) originated from accounts of individuals who self-identified as International Board Certified Lactation Consultants (IBCLCs). On average, the content corresponded to a ninth-grade reading level per Flesch-Kincaid Grade Level. Quality of posts as rated by the JAMA Benchmark Criteria corresponded to a median score of 3.0 (IQR, 2.0-4.0). Of the 71 posts, only 8 (11.3%) contained no misinformation. There was a significant difference in misinformation prevalence between accounts run by IBCLCs vs non-IBCLCs, with posts from IBCLCs less likely to contain over 50% misinformation (odds ratio, 0.22; 95% CI, 0.06-0.81), compared with posts from non-IBCLCs. Conclusions and Relevance: This study found a high frequency of misinformation in social media content on ankyloglossia. Most content was generated by self-identified health care practitioners but not physicians. Furthermore, the grade level of the content reviewed was above that recommended for the public. As the public increasingly looks to social media for medical information, health care practitioners should correct medical misinformation.
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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 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".