Nipple Pain during Breastfeeding with or without Visible Trauma
Bibliographic record
Abstract
BACKGROUND: Nipple pain is a major cause of early weaning. The causes of nipple pain are diverse, and most treatments involve experience-based assessment. There is little knowledge of the intensity or variation in pain experienced by breastfeeding women. Given the high breastfeeding initiation rates, it is important to evaluate pain experienced by lactating women in detail. OBJECTIVE: To investigate and compare the pain experienced by breastfeeding women using objective measures. METHODS: The type, effect, and severity of pain were measured using the McGill Pain Questionnaire, Brief Pain Inventory, and Visual Analogue Scale, respectively, for 2 groups of breastfeeding women. One group were experiencing persistent nipple pain despite treatment, and the other had obvious signs of nipple trauma. RESULTS: Pain intensity and interference scores were highly variable for both groups. Mothers with nipple trauma reported significantly higher mean pain intensity and breastfeeding interference. Higher pain intensity scores were related to higher interference scores. After accounting for pain intensity, higher interference with general activity, mood, and sleep interference was related to longer duration of pain. There was no difference in MPQ class scores. CONCLUSIONS: The ramifications of nipple pain extend far beyond the act of breastfeeding, particularly for women whose pain lasts several months. Given the lack of evidence-based treatments, it is not surprising that pain is a major contributor to premature weaning. Further research into the causes of nipple pain is necessary to enable the implementation of effective interventions, thus reducing further complications such as infection and postnatal depression. Detailed pain analysis may assist in assessing the success of these interventions.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".