Alternative Therapies for Ankyloglossia-Associated Breastfeeding Challenges: A Systematic Review
Bibliographic record
Abstract
Background: Ankyloglossia (AG) diagnoses are increasingly common, and management is not standardized. Nonsurgical alternative therapies are frequently recommended in conjunction with or instead of frenotomy, with uncertain evidence. Objective: To evaluate the efficacy of nonsurgical alternative therapies (chiropractic care, myofunctional therapy, and osteopathy) in improving breastfeeding for infants diagnosed with AG. Methods: PubMed, Embase, CINAHL, Scopus, Web of Science, Clinicaltrials.gov, and Google Scholar were searched (September–October 2023). Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. A librarian-designed search included the terms “Ankyloglossia,” “Non-surgical,” “myofunctional therapy,” “chiropractic,” “osteopathy,” and related therapies, with no date restrictions. English language studies of infants <24 months with AG and alternative therapy were included. Risk-of-bias evaluation used Newcastle–Ottawa Scale (NOS). Results: Of 1,304 identified articles, four studies (2016–2022) met inclusion criteria (two cross-sectional, one case report, and one case series). All studies reported frenotomy in combination with alternative therapy yielded favorable outcomes for maternal pain, weight gain, feeding duration, and maintenance of latch. The risk of bias was moderate for two studies, low for the case series, and not calculated for the case report, which has an inherent high risk of bias. All studies lacked control or comparator groups preventing definitive conclusions about the role of alternative therapies in AG. Conclusion: Although some studies suggest the potential benefits of combining alternative therapies with surgery for AG-related breastfeeding issues, the lack of control groups renders the evidence inconclusive. Nonsurgical approaches alone currently lack sufficient evidence. As these alternative therapies gain popularity, rigorous research is crucial to determine their cost-effectiveness and role in managing AG.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".