Comparison of Lanolin and Human Milk Treatment of Painful and Damaged Nipples: A Randomized Control Trial
Bibliographic record
Abstract
Background: Painful and damaged nipples are frequently associated with breastfeeding cessation in the early postpartum period. The results of researchers’ studies utilizing different treatments have been inconclusive. Research Aim: To compare the intensity of nipple pain and the healing of damaged nipples during the first 10 days postpartum using either lanolin or human milk treatments. Methods: This single-blind randomized controlled trial included participants ( N = 206) who were primiparous with painful and damaged nipples. Participants were recruited from the tertiary teaching hospital within the first 72 hr after delivery and randomized to the intervention group with lanolin ( n = 103) and a human milk control group ( n = 103). Data were collected in the maternity ward, 3 and 7 days after randomization. The primary outcome was nipple pain intensity and quality measured 3 and 7 days after randomization by the McGill Pain Questionnaire – short form. The nipple damage self-assessment questionnaire was used for the assessment of nipple healing. Breastfeeding self-efficacy, breastfeeding duration, and exclusivity were assessed as secondary outcomes. Results: Participants in both groups reported a statistically nonsignificant reduction in pain (quality and intensity of pain) as well as improved nipple healing 7 days after randomization. Participants in the lanolin group exclusively breastfed their infants 3 days after randomization—significantly more often than participants in the control group ( p = .026). The study did not reveal any statistically significant differences for other secondary outcomes. Conclusion: Both lanolin and human milk are equally effective in treating painful and damaged nipples. Registered with Clinicaltrials.gov (NCT04153513)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".