Timing of repair and mesh use in traumatic abdominal wall defects: a systematic review and meta-analysis of current literature
Bibliographic record
Abstract
Background: Traumatic abdominal wall hernias or defects (TAWDs) after blunt trauma are rare and comprehensive literature on this topic is scarce. Altogether, there is no consensus about optimal methods and timing of repair, resulting in a surgeon's dilemma. The aim of this study was to analyze current literature, comparing (1) acute versus delayed repair and (2) mesh versus no mesh repair. Methods: A broad and systematic search was conducted in PubMed, EMBASE, and the Cochrane Library. The selected articles were assessed on methodological quality using a modified version of the CONSORT 2010 Checklist and the Newcastle-Ottawa scale. Primary endpoint was hernia recurrence, diagnosed by clinical examination or CT. Random effects meta-analyses on hernia recurrence rates after acute versus delayed repair, and mesh versus no mesh repair, were conducted separately. Results: In total, 19 studies were evaluated, of which 6 were used in our analysis. These studies reported a total of 229 patients who developed a TAWD, of whom a little more than half underwent surgical repair. Twenty-three of 172 patients (13%) who had their TAWD surgically repaired developed a recurrence. In these studies, nearly 70% of the patients who developed a recurrence had their TAWD repaired primarily without a mesh augmentation and mostly during the initial hospitalization. Pooled analysis did not show any statistically significant favor for either use of mesh augmentation or the timing of surgical repair. Conclusion: Although 70% of the recurrences occurred in patients without mesh augmentation, pooled analysis did not show significant differences in either mesh versus no mesh repair, nor acute versus delayed repair for the management of traumatic abdominal wall defects. Therefore, a patient's condition (e.g., concomitant injuries) should determine the timing of repair, preferably with the use of a mesh augmentation.
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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 | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.035 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".