Implant-based breast surgery and capsular formation: when, how and why?—a narrative review
Bibliographic record
Abstract
Background and Objective: Implant-based breast surgery is a common procedure for both reconstructive and aesthetic purposes. Breast implants, like any foreign object, trigger the formation of a capsule around them. While generally harmless, the capsule can undergo fibrotic changes leading to capsular contracture, which can negatively impact surgical outcomes and patient well-being. Additionally, rare but serious complications, such as breast implant-associated anaplastic large cell lymphoma (BIA-ALCL) and capsule-associated squamous cell carcinoma, have been reported. This paper aims to review the physiology of capsular formation, identify factors contributing to capsule-related pathologies, and discuss their clinical implications. Methods: A review of relevant literature was conducted by searching databases for articles published between inception and September 2022. The search included but not limited to terms such as "capsular formation" and "capsular contracture". Selected articles were critically analyzed to address the objectives of this review. Key Content and Findings: Capsular formation involves interactions between the implant surface, surrounding tissues, and the immune system. Factors influencing pathological changes in the capsule include genetic predisposition, bacterial contamination, implant characteristics, and surgical techniques. Capsular contracture, characterized by tissue hardening, pain, and implant distortion, remains the most common complication. Rare but life-threatening conditions, such as BIA-ALCL and capsule-associated squamous cell carcinoma, necessitate vigilant monitoring and early detection. Conclusions: Understanding the physiology of capsular formation and its associated pathologies is crucial for healthcare providers involved in implant-based breast surgery. Efforts should focus on minimizing the risk of capsular contracture through improved implant materials, surgical techniques, and infection prevention. The emergence of BIA-ALCL and capsule-associated squamous cell carcinoma underscores the importance of long-term surveillance and prompt diagnosis. Further research is needed to uncover underlying mechanisms and develop preventive measures and treatments for these complications. Enhancing our knowledge and clinical management of capsular formation will lead to safer and more successful outcomes in implant-based breast surgery.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".