Persistent lactation in bilateral breast implant augmentation: A case report and review of the literature
Bibliographic record
Abstract
Background: Persistent lactation, or galactorrhoea, is a common problem which is infrequently seen in the setting of aesthetic surgery. Increasing frequency of aesthetic breast surgery such as breast augmentation suggests a need for improved understanding of the effect of galactorrhoea on surgical outcomes. Case Report: A 34-year-old patient underwent day-case bilateral breast reduction/mastopexy combined with sub-muscular implant augmentation, abdominoplasty and bilateral liposuction to the flanks. She reported to have stopped breastfeeding more than 6 months prior. Intraoperatively, the breast tissue was noted to be lactating. The procedure was completed as planned and a routine postoperative plan was followed including oral antibiotics, analgesia and compression garments. The patient was discharged, however reattended on postoperative day 10 with breast pain and fevers. She was treated for right breast surgical site infection and required washout and implant removal. She was referred to Endocrinology for treatment of galactorrhoea with Bromocriptine and Cabergoline. She subsequently underwent revision implant augmentation with good outcomes. Discussion: This case highlights the increased likelihood of post-operative infection in galactorrhoea associated with breast implant augmentation. It is important to exclude lactation preoperatively and avoid a prosthesis in this situation, to minimise this risk and optimise surgical outcomes. Conclusion: Aesthetic breast surgeons must be aware of the incidence of galactorrhoea, and its possible effects on risks of postoperative complications and poor aesthetic outcomes. The authors suggest deferring implant augmentation until complete resolution of lactation where possible.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".