Breastfeeding Outcome and Complications in Females With Breast Implants: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Breast augmentation is a commonly performed cosmetic procedure. We set out to determine whether there was any effect on breastfeeding in females after breast implants. The aim of this study was to perform a systematic review and meta-analysis of the current evidence on breastfeeding outcome and complications in females with breast augmentation. A systematic review was performed utilizing MEDLINE, EMBASE, and all evidence-based medicine reviews from their respective inception dates to November 7, 2022, to assess outcomes of breastfeeding in females with breast implants (PROSPERO ID: CRD42022357909). This review was in accordance with both the Cochrane Handbook for Systematic Review of Interventions and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Eleven studies (4 prospective and 7 retrospective) in total were included in the review. A total of 8197 out of 9965 (82.25%) patients were successfully able to breastfeed after breast implants. Of 5 studies that included a control group, 343,793 of 388,695 (88.45%) women without breast implants successfully breastfed. A meta-analysis of 5 comparative studies showed a significant reduction of breastfeeding in females with breast implants, n = 393,686, pooled odds ratio = 0.45 (95% CI, 0.38 to 0.53). Complications described included pain, mastitis, insufficient or excessive lactation, and nipple inversion. There may be impairment in ability to breastfeed for females who receive breast implants when compared with those without. Additional studies on the topic are needed to further clarify the relationship.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.037 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".