Breast shells for pain and nipple injury prevention: A non-randomized clinical trial
Bibliographic record
Abstract
Objective This study aimed to analyze the effectiveness of breast shells in preventing pain and nipple injury during breastfeeding. Method A non-randomized clinical trial was carried out with blinding to the evaluators of the study results. The study included women with ≥35 weeks of singleton pregnancy, no nipple changes, and a desire to breastfeed. Resulting in 62 lactating women. The experimental group used breast shells and health education with clinical demonstration ( n = 29), whereas the control group used no breast shells ( n = 33). Pain and nipple injury were assessed three times, twice prenatally and once up to 14 days postpartum. Results Nipple injury (50.0%) and nipple pain (67.7%) presented with similar frequency in both groups ( p = 1). Breast engorgement (35,5%) was associated with nipple pain ( p = 0.019 ) and its onset was delayed in the experimental group ( p = 0.001). Health education contributes to breast and nipple care and increases favorable breastfeeding patterns. Conclusion Breast shells do not prevent nipple pain or injury. Innovation As far as we know, this is the first clinical research evaluating the use of breast shells since the antenatal care to prevent the occurrence of nipple pain and injury.
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 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) | 0.003 | 0.002 |
| 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 | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".