Outcomes of Tongue Reduction Surgery in Beckwith-Wiedemann Syndrome: A Systematic Review
Bibliographic record
Abstract
INTRODUCTION: Macroglossia is a frequent clinical feature of Beckwith-Wiedemann syndrome (BWS), a congenital overgrowth disorder. Macroglossia can lead to abnormal breathing, feeding, speech, and dentoskeletal development. Partial glossectomy is a common intervention aimed at reducing these abnormalities. The optimal timing of partial glossectomy remains controversial due to the potential need for secondary surgery and the ongoing growth of the tongue in early childhood. MATERIALS AND METHODS: After PRISMA-ScR and PRISMA-S reporting standards, this systematic review included English language studies of patients with BWS who underwent partial glossectomy. Data were extracted, including patient age, clinical outcomes, and follow-up. Study evidence levels were categorized based on a recognized hierarchy, and bias was assessed using the MINORS criteria. RESULTS: Early tongue reduction surgery (<24 mo) was associated with a lower incidence of class 3 occlusion and anterior open bite compared with later surgery. Improvements in speech intelligibility, tongue mobility, and breathing outcomes, including a reduction in obstructive sleep apnea, were observed, especially in early surgical groups. Feeding and drooling outcomes improved across both early and late surgical interventions, although no direct comparisons were made between the 2. Overall, tongue reduction surgery demonstrated benefits in functional outcomes, whereas dentoskeletal improvements remained variable. CONCLUSION: Although there is a lack of consensus to the optimal age for the procedure, overall tongue reduction surgery in BWS seems to have functional benefits, including in speech, feeding, and breathing. Dentoskeletal outcomes are more variable. Variability in macroglossia severity, surgical technique, and surgeon experience may account for differences in reported outcomes across studies.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".