Onabotulinum Toxin A (BoNT‐A) for Drooling in Children: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
INTRODUCTION: Sialorrhea, also known as drooling, hypersalivation, or ptyalism, has a significant impact on the medical and psychosocial well-being of children. Onabotulinum toxin A (BoNT-A) is the most commonly used botulinum toxin worldwide for the treatment of sialorrhea in children. OBJECTIVES: To conduct a comprehensive systematic review and meta-analysis to assess the clinical efficacy and potential adverse effects of BoNT-A as a treatment for drooling in children. METHODS: Cochrane, Embase, and Medline databases were systematically searched (up to May 2023). Out of 535 identified publications, 20 were found eligible for inclusion. A systematic review and meta-analysis were performed to determine the efficacy of BoNT-A treatment in children in reducing the frequency and severity of drooling. RESULTS: Out of the 20 studies included, a meta-analysis was conducted on the complete dataset of eight studies involving 131 patients. BoNT-A was found to significantly decrease the severity of drooling in patients with sialorrhea (standardized mean difference [SMD], -2.07; 95% confidence interval [CI], -2.91 to -1.23; p < 0.0001) when compared with the conditions before injections using random-effects models. Six studies out of 20 reported dysphagia as an adverse effect after injection. Other side effects included thickness of saliva and pain at the site of injection. CONCLUSION: BoNT-A is a clinically effective therapy that improves drooling severity in children with sialorrhea. Although there were some adverse side effects reported, they were transient and not severe. Future studies are needed to further evaluate the best techniques and to identify the ideal dosages required to achieve the optimal outcomes. Laryngoscope, 134:3012-3017, 2024.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.035 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".