A Surgical Technique for Treating Submuscular and Subglandular Symmastia Combining Standard Techniques with An Aggressive Medial Capsulorrhaphy
Bibliographic record
Abstract
Symmastia, the medial confluence of breast implants producing a ‘bread loaf’ quality, has continued to be a vexing complication of breast reconstruction and breast augmentation, despite numerous proposed repair methods. Previous procedures include capsulorrhaphy, allograft or xenograft sling suspensions, mastopexies and, in cases of subglandular implants, collapsing the pocket and reinserting the implant beneath the pectoralis major muscle. In the present article, the authors outline a reliable long-term correction of submuscular symmastia, combining the techniques of capsulorrhaphy, use of porcine xenograft slings, submuscular placement of the implants, and use of capsular tissue folded and sutured onto the presternal fascia with nonabsorbable sutures. This technique places the porcine xenograft sling between the implant and the folded capsular tissue sutured with nonabsorbable sutures, protecting the implant from erosion/puncture by the nonabsorbable sutures. The authors also propose a similar technique for the correction of subglandular symmastia. The patient in the current article presented with submuscular symmastia and has been followed for <4 years postoperatively with absolutely no recurrence of symmastia, even with forcible application of lateral pressure on her breasts in an attempt to reproduce medial confluence. The technique offers a promising solution to the frustrating difficulty of symmastia, a complication with a high recurrence rate despite many proposed repairs across decades.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".