Implant Texture and Capsular Contracture: A Review of Cellular and Molecular Pathways
Bibliographic record
Abstract
Background: Capsular contracture (CC) is a leading cause of morbidity in implant-based breast surgery. Implant surface texture has been implicated in CC development, yet its etiopathogenesis remains unclear. We conducted a systematic review to determine the influence of implant surface texture on cellular and molecular mechanisms involved in the etiopathogenesis of CC. Methods: A systematic review of the MEDLINE, Embase, Web of Science, and Scopus databases was completed to examine the influence of implant texture on cellular and molecular pathways leading to CC. Excluded articles were reviews and those examining solely the clinical presentation of CC. Results: Development of CC includes prolonged inflammation, increased myofibroblast density, parallel arrangement of collagen fibers, and biofilm formation. When compared with textured implants, smooth implants are associated with reduction in parallel collagen, capsule thickness, and sheer frictional force. Microtextured implants trigger a reduced macrophage response and decreased fibroblast activation as compared with smooth and macrotextured surfaces. Bacterial counts on microtextured and smooth surfaces are significantly lower than that of macrotextured surfaces. Both micro- and macrotextured implants have increased matrix metalloproteinases and activation of tumor necrosis factor α pathway, with increased activation of the transforming growth factor β1 pathway relative to smooth implants. Conclusions: Implant surface texture alters the cellular and molecular mechanisms in the chronic inflammatory process leading to CC. Given the complex biological system of cellular and molecular events in CC, a mathematical model integrating these influences may be optimal to deduce the etiopathogenesis.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".