Late Seromas after Breast Implants
Bibliographic record
Abstract
BACKGROUND: Late seromas surrounding breast implants are becoming an increasingly important issue in breast surgery. The authors report their experience with late seromas and describe their previous management options. METHODS: A multicenter retrospective review of patients who developed late seromas (clinically presenting seromas without evidence of overt or documented infection more than 1 year after implant operation) was performed. Management, surgical technique, outcomes, complications, culture findings, and cytology results were recorded. RESULTS: Between 2005 and 2010, 28 late seromas were identified in 25 patients. The average interval from the patient's last surgery to seroma onset was 4.7 years; 27 of 28 breasts (96 percent) had a Biocell textured device in place at the time of seroma development. The late seromas in the series were managed as follows: 15 (53.6 percent) by complete capsulectomy, seroma drainage, and new implant placement; three (10.7 percent) by seroma drainage and new implant placement but without capsulectomy; two (7.1 percent) by complete capsulectomy and seroma drainage but without implant replacement; five (17.9 percent) by only ultrasound-guided seroma drainage without the need for surgical intervention; and three (10.7 percent) by antibiotic therapy alone. All cultures and cytology studies were negative for malignancy or infection; 27 of 28 seromas (96 percent) were treated successfully by one of the described approaches. CONCLUSIONS: Biocell textured implants were more likely to be associated with late seromas than were smooth shell implants. The overwhelming majority of late seromas appear to be idiopathic, without clear evidence of infection or malignancy. A graduated approach, including several different management strategies, was used to successfully manage these patients. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, IV.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".