Is Ankyloglossia Correlated With Pediatric Sleep Disordered Breathing? A Systematic Review
Bibliographic record
Abstract
OBJECTIVES: Sleep disordered breathing (SDB) affects 2%-11% of children, predisposing them to neurobehavioral and developmental consequences. Ankyloglossia has been proposed as a risk factor for SDB, and frenotomy as a treatment for SDB in children with ankyloglossia. With increasing ankyloglossia diagnoses, it is critical to evaluate the evidence for a linkage between SDB and ankyloglossia. DATA SOURCES: EMBASE, Web of Science, Medline, CINAHL, CCRCT, and SCOPUS were searched from inception to February 13, 2025. Publications assessing the relationship between ankyloglossia and SDB in non-syndromic children ages 0 to 18 years were included. Eight studies involving 1171 patients met inclusion criteria. REVIEW METHODS: Two reviewers independently screened abstracts and full texts for inclusion. Strength of clinical data was graded according to the Cochrane Risk of Bias Assessment and modified Newcastle-Ottawa Scale. RESULTS: There is mixed evidence of a relationship between ankyloglossia and pediatric SDB. The lack of standardized diagnostic criteria for ankyloglossia and the use of surveys instead of validated clinical assessment tools to assess SDB limit the generalizability of findings. There is also insufficient data to conclude that frenotomy is indicated in managing SDB in children with ankyloglossia. While two interventional studies report a positive association, their results have limited validity and generalizability. CONCLUSION: There is an unclear relationship between ankyloglossia and pediatric SDB and insufficient evidence to determine if frenotomy is indicated as a treatment for SDB in children with ankyloglossia. Higher quality studies with standardized functional measures of ankyloglossia and validated assessment of SDB are needed. LEVEL OF EVIDENCE: N/A.
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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 | 0.008 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".