Complications of Acellular Dermal Matrices in Abdominal Wall Reconstruction
Bibliographic record
Abstract
BACKGROUND: Acellular dermal matrices have been increasingly used in abdominal wall reconstruction. Unique characteristics of these grafts have allowed surgeons to reconstruct increasingly complex abdominal wall defects. This has resulted in a myriad of complications related to the acellular dermal matrix with unique management strategies as compared with synthetic mesh. METHODS: A review of the literature was performed to identify studies evaluating the use and efficacy of acellular dermal matrices in abdominal wall reconstruction. Complication profiles were identified and are compared. Differences between porcine and human derivatives were identified. RESULTS: Hernia recurrence, infection, skin necrosis, and fluid collections were among the most common complications following abdominal wall reconstruction identified in the literature. Differences among various acellular dermal matrix products make certain types more suitable for abdominal wall reconstruction. CONCLUSIONS: Complications are frequent in abdominal wall reconstruction. Many acellular dermal matrices have properties that allow for conservative management and maintenance of reconstruction when complications occur. With traditional synthetic mesh reconstruction, complications likely result in mesh explantation and prolonged morbidity. With acellular dermal matrix reconstruction, however, more conservative treatment strategies will allow for mesh salvage.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".