The impact of breast reduction surgery on breastfeeding: Systematic review of observational studies
Bibliographic record
Abstract
BACKGROUND: Almost half a million breast reduction surgeries are performed internationally each year, yet it is unclear how this type of surgery impacts breastfeeding. This is particularly important given the benefits of breastfeeding. OBJECTIVES: To determine if breast reduction surgery impacts breastfeeding success and whether different surgical techniques differentially impact breast feeding success. METHODS: Databases were searched up to September 5, 2017. Studies were included if they reported the number of women successful at breastfeeding or lactation after breast reduction surgery, and if they reported either the total number of women who had children following breast reduction surgery, or the total number of women who attempted to breastfeed following surgery. RESULTS: Of 1,212 studies, 51 studies met the inclusion criteria; they were located worldwide and had 31 distinct breast reduction techniques. The percentage of breastfeeding success among studies was highly variable. However, when analyzed by the preservation of the column of parenchyma from the nipple areola complex to the chest wall (subareolar parenchyma), a clear pattern emerged. The median breastfeeding success was 4% (interquartile range (IQR) 0-38%) for techniques with no preservation, compared to 75% (IQR 37-100%) for techniques with partial preservation and 100% (IQR 75-100%) for techniques with full preservation. CONCLUSIONS: Techniques that preserve the column of subareolar parenchyma appear to have a greater likelihood of successful breastfeeding. The preservation of the column of subareolar parenchyma should be disclosed to women prior to surgery. Guidelines on the best breast reduction techniques to be used in women of child bearing years may be advantageous to ensure women have the greatest potential for successful breastfeeding after breast reduction surgery.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| 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".