Complications and Satisfaction After Adolescent Breast Reduction for Juvenile Macromastia: Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background: Juvenile macromastia is a rare condition of significant breast enlargement in adolescents. Reduction mammoplasties offer relief, but data on complications in adolescents are rare as opposed to data on adults. We reviewed the outcomes, complications, recurrence, and patient satisfaction after reduction mammoplasties in adolescents. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we conducted a thorough search across various electronic databases for "juvenile macromastia" and "breast reduction." Only studies on women diagnosed with juvenile macromastia before 21 years of age who underwent breast reduction surgery were included. The methodological index for nonrandomized studies was used to assess study quality. Results: This meta-analysis pooled data from 11 studies. The overall pooled postoperative complication rate, based on random-effects models, was 17.5% (95% confidence interval: 9.7%-29.5%). The recurrence rate was 15.6% (95% confidence interval: 8.5%-26.9%), ranging from 0% to 52.9%. Complication rates varied widely across studies, with wound dehiscence, hematoma, and infection being the most common. Severe complications, such as nipple necrosis, were rare. Low publication bias was observed for postoperative complications, but potential bias was noted for recurrence outcomes. Conclusions: The findings emphasize the need for standardized reporting and long-term follow-up to improve the reliability of pooled estimates and to guide clinical decision-making. The high recurrence risk emphasized the need for individualized surgical approaches and careful management of risk factors, such as obesity and smoking, to improve outcomes. Despite the variability, the benefits of surgery generally outweighed the risks, with high patient satisfaction reported in the included studies.
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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 | 0.014 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.040 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".