Prophylactic Onlay Mesh Repair (POMR) Versus Primary Suture Repair (PSR) for Prevention of Incisional Hernia (IH) After Abdominal Wall Surgery: A Systematic Review and Meta‐analysis
Bibliographic record
Abstract
BACKGROUND: With many different operative techniques in use to reduce the incidence of incisional hernias (IH) following a midline laparotomy, there is no consensus among the clinicians on the efficacy and safety of any particular repair technique. This meta-analysis compares the prophylactic onlay mesh repair (POMR) and primary suture repair (PSR) for the incidence of IH. METHODS: A meta-analysis and systematic review of MEDLINE, PubMed Central (via PubMed), Embase (via Ovid), SCOPUS, ScienceDirect, Google Scholar, SCI and Cochrane Library databases were undertaken. Seven randomized controlled trials assessing the outcomes of PSR and POMR were analyzed in accordance with the PRISMA statement. The risk of bias was assessed using the Rob2 tool. RESULTS: According to the pooled analysis, POMR significantly reduced the incidence of IH compared to the PSR (OR 5.82 [95% CI 2.69, 12.58] P < 0.01) with a significantly higher seroma formation rate post-surgery (OR 0.35 [95% CI 0.18, 0.67] P < 0.01). Furthermore, the length of hospital stay (WMD -0.78 [95% CI -1.58, 0.02] P = 0.05) was significantly shorter for PSR compared to POMR group. Comparable effects were noted for reintervention, postoperative ileus, postoperative hematoma, postoperative mortality, long-term intervention and long-term deaths between the two groups. CONCLUSIONS: POMR significantly reduces the risk of IH when compared to the PSR, with an increased risk of postoperative seroma formation and longer hospital stay. However, more RCTs with standardized protocols are needed for meaningful comparisons of the two interventions, along with longer duration of follow-up to assess the impact on the occurrence of IH.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.031 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".