An All-Purpose Nipple Ointment Versus Lanolin in Treating Painful Damaged Nipples in Breastfeeding Women: A Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: The negative outcomes associated with painful and damaged nipples have been widely documented in the breastfeeding literature. Numerous studies have been conducted evaluating topical preparations to treat nipple pain and damage with equivocal findings. No studies have evaluated the effectiveness of the increasingly popular all-purpose nipple ointment (APNO). The purpose of this trial is to evaluate the effect of the APNO versus lanolin on nipple pain among breastfeeding women with damaged nipples. SUBJECTS AND METHODS: A double-blind, randomized controlled trial was conducted in a large single-site, tertiary-care hospital in Toronto, ON, Canada. Breastfeeding women (n=151) identified as having damage to one or both nipples were randomized to apply either APNO (intervention group) or lanolin (control group) to their nipples according to the trial protocol. The primary outcome was nipple pain at 1 week after randomization measured using the Short Form McGill Pain Questionnaire. Additional outcomes at 1 week after randomization and 12 weeks postpartum included nipple yeast symptoms and/or mastitis, rates of breastfeeding duration and exclusivity, and maternal satisfaction with infant feeding method and treatment ointment. RESULTS: There were no significant group differences in mean pain scores at 1 week after randomization. Women in the lanolin group reported significantly greater satisfaction with their infant feeding method and had nonsignificantly higher breastfeeding duration and exclusivity rates at 12 weeks postpartum. CONCLUSION: Results suggest that APNO is not superior to lanolin in treating painful, damaged nipples.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".