Frenotomy in Infants with Tongue-Tie and Breastfeeding Problems
Bibliographic record
Abstract
Background: Infant tongue-tie can cause breastfeeding problems, which may be improved by frenotomy. However, controversy exists among the medical community. Research aim: To examine the influence of frenotomy on infants with posterior ankyloglossia, by quantifying the changes in breastfeeding and maternal nipple pain using standardized tools. Methods: Newborns ( N = 30) below 12 weeks of age were recruited from the Herzl–Goldfarb Breastfeeding Clinic between April 2014 and April 2015. Diagnosis of posterior ankyloglossia was made clinically. Frenotomy was performed. Mothers received breastfeeding counseling before and after the procedure. Pain and breastfeeding were assessed before the procedure, immediately after the procedure, and after 2 days, 7 days and 14 days. Breastfeeding was assessed using the LATCH Tool and by subjective questioning. Maternal nipple pain was assessed using the Numeric Rating System. Results: No complications were reported with frenotomy. There was a significant improvement in LATCH score immediately post-frenotomy, with an increase in median scores from 7.5 to 8.5 ( p < .0001, Wilcoxon signed rank test). There was a significant decrease in median pain score immediately post-frenotomy, from 3.0 on the left nipple and 3.25 on the right nipple, to 0 bilaterally ( p < .0001, Wilcoxon signed rank test). Subjective improvement in breastfeeding was reported by 90% of mothers immediately after frenotomy and 83% of mothers at Day 14. Conclusion: Frenotomy for posterior ankyloglossia may improve breastfeeding and nipple pain.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".