Stimulation of Lactation Using Acupuncture: A Case Study
Bibliographic record
Abstract
INTRODUCTION: Breastfeeding is a recognized preferred method of infant feeding; however, for many women, difficulties in breastfeeding result in termination before the recommended period of time. Acupuncture is suggested to be a promising option to treat lactation insufficiency and enhance the production of maternal milk. MAIN ISSUE: We have reported the case of a woman with lactation insufficiency due to Caesarean section and congenital unilateral invaginated nipple. Milk production started on the 3rd day following delivery. The newborn was not provided with any food or fluids other than mother's milk. At 5 days of life, the newborn required long feeding periods and lost 4% of his birth weight, with the participant reporting lactation insufficiency described by the perception of inadequate milk production. MANAGEMENT: Despite the implementation of conventional measures to improve lactation, the difficulties in breastfeeding persisted. Acupuncture was tried on Day 6 of life, and enhanced milk production was observed, which could be measured as the volume of residual milk extracted using the breast pump each time after the newborn achieved satiety. After acupuncture treatment there was an augmentation of maternal milk production from both breasts and successful lactation. CONCLUSION: This case study provides information that might be useful for prospective investigation of acupuncture's efficacy in women with lactation insufficiency.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".