Systematic review of the evidence for resolution of common breastfeeding problems—Ankyloglossia (Tongue Tie)
Bibliographic record
Abstract
AIM: Tongue tie is a common problem affecting breastfeeding due to poor infant latch and/or maternal pain. Evidence of whether treatment improves breastfeeding outcomes is conflicting. We conducted a systematic review and meta-analysis to examine the effectiveness of tongue-tie treatment on breastfeeding difficulties. METHODS: We searched peer-reviewed and grey literature in MEDLINE (OVID), PubMed, CINAHL Plus, EMBASE and PsycINFO, from 01/1970 to 09/2019. INCLUSION: randomised and non-randomised clinical trials, and quasi-experimental study designs, involving breastfeeding interventions for full-term singleton infants, using standardised measure of breastfeeding difficulty. EXCLUSION: qualitative and purely observational studies, lacked operational definition of breastfeeding difficulty, lacked control/comparison group. We assessed risk of bias, summarised study quality and results and conducted meta-analysis using random effects modelling. RESULTS: Six studies on tongue-tie division were included (4 randomised and 2 non-randomised). Meta-analysis of standardised mean differences in breastfeeding difficulty scores in four studies showed statistically significant differences in favour of frenotomy (Pooled SMD +2.12, CI:(0.17-4.08)p = 0.03). Similarly, a statistically significant difference in favour of frenotomy was observed for pain (Pooled SMD -1.68, 95% CI: (-2.87- -0.48). CONCLUSION: Results support that infant frenotomy is effective for improving standardised scores on breastfeeding difficulty and maternal pain scales and could improve breastfeeding outcomes.
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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 | 0.012 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.013 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".