Systematic review and network meta-analysis of methods of mesh fixation during laparoscopic ventral hernia repair
Bibliographic record
Abstract
BACKGROUND: Ventral hernia repairs are common and have high recurrence rates. They are usually repaired laparoscopically with an intraperitoneal mesh, which can be fixed in various ways. The aim was to evaluate the recurrence rates for the different fixation techniques. METHODS: This systematic review included studies with human adults with a ventral hernia repaired with an intraperitoneal onlay mesh. The outcome was recurrence at least 6 months after operation. Cohort studies with 50 or more participants and all RCTs were included. PubMed, Embase and the Cochrane Library were searched on 22 September 2016. RCTs were assessed with the Cochrane risk-of-bias assessment tool and cohort studies with the Newcastle-Ottawa scale. Studies comparing fixation techniques were included in a network meta-analysis, which allowed comparison of more than two fixation techniques. RESULTS: Fifty-one studies with a total of 6553 participants were included. The overall crude recurrence rates with the various fixation techniques were: absorbable tacks, 17·5 per cent (2 treatment groups); absorbable tacks with sutures, 0·7 per cent (3); permanent tacks, 7·7 per cent (20); permanent tacks with sutures, 6·0 per cent (25); and sutures, 1·5 per cent (6). Six studies were included in a network meta-analysis, which favoured fixation with sutures. Although statistical significance was not achieved, there was a 93 per cent chance of sutures being better than one of the other methods. CONCLUSION: Both crude recurrence rates and the network meta-analysis favoured fixation with sutures during laparoscopic ventral hernia repair.
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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 | 0.036 | 0.105 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.048 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".