Biosynthetic meshes in contaminated fields: where are we now? Asystematic review and meta-analysis in humans
Bibliographic record
Abstract
PURPOSE: Selection of an appropriate mesh reinforcement for hernia repair in contaminated fields is a significant problem for surgeons. To date the proper mesh for contaminated fields has not been found. Biosynthetic meshes have emerged as new treatment option in contaminated fields. This study aims to evaluate the postoperative outcomes of biosynthetic meshes in contaminated fields. METHODS: Systematic electronic search (PubMed, Medline, Embase, Scopus), according to PRISMA criteria, was performed. A literature search of scientific papers was performed by two reviewers until April 2021. Articles were chosen based on reference to biosynthetic meshes, their use in infected fields, and in human subjects. GRADE methodology and the modified Newcastle-Ottawa scale were used to assess the quality of studies. According to CDC-Centers for Disease Control classes patients were divided into two subgroups, group 1 (CDC class 2) and group 2 (CDC classes 3-4). RESULTS: The research included 21 articles and 1619 patients were analyzed. Long-term follow-up showed a significant higher recurrence rate than short-term follow-up. P < 0.001. Meta-analysis of these studies showed that the SSI were significantly higher in CDC classes 3-4 than CDC class 2 (P < 0.01). No differences were found in SSO (P = 0.06) and recurrence (P = 0.37) rate among the two groups. PhasixTM was the most common mesh in 15 studies. The mean follow-up was 23.0 months. The surgical site infection (SSI) rate was 17.3\%. The surgical site occurrence (SSO) rate was 32.4\%. Recurrence rate was 11.5\%. CONCLUSION: This is the first systematic review and meta-analysis on the clinical outcomes of abdominal wall repair using biosynthetic mesh in contaminated-infected settings. The results show good results in patients at high risk of postoperative wound complications. The aim of this study is to add to the growing literature on biosynthetic mesh a picture of current literature evidence to help future researchers performing further studies on this topic.
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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 | 0.019 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.020 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".