Pain and Surgical Outcomes Reporting After Laparoscopic Ventral Hernia Repair in Relation to Mesh Fixation Technique: A Systematic Review and Meta-Analysis of Randomized Clinical Trials
Bibliographic record
Abstract
OBJECTIVES: The aim of this meta-analysis was to examine postoperative pain and surgical outcomes (operative time, hospital stay, the incidence of seroma and recurrence) with different mesh fixation methods following laparoscopic ventral hernia repair (LVHR). METHODS: Randomized clinical trials compared different methods of mesh fixation in LVHR and reported on pain outcome measures analyzed. The results were expressed as odds ratio (OR) for combined dichotomous and mean difference (MD) for continuous data. RESULTS: Five randomized controlled trials (RCTs) with a total of 466 patients comparing tack mesh fixation to suture mesh fixation technique in LVHR were identified, all were published after 2005. A meta-analysis gave statistically similar odds of postoperative chronic pain (OR, 1.24; 95% CI, 0.65-2.38; z = 0.65; P = .51). No difference in pain intensity (PI) scores was found at 4-6 weeks (MD, 0.18;% CI, -0.48 to 0.85; z = 0.54; P = .59) and at 3-6 months postoperatively (MD, 0.10; 95% CI, -0.21 to 0.42; z = 0.64; P = .52). There was no difference in the pooled analysis of seroma/hematoma formation (OR, 0.60; 95% CI, 0.29-1.26; z = 1.35; P = .18), recurrence (OR, 1.11; 95% CI, 0.34-3.62; z = 0.18; P = .86), and hospital stay (MD, -0.06; 95% CI, -0.19 to 0.08; z = 0.83; P = .40). Operative time was significantly lower with tack fixation (MD, -19.25; 95% CI, -27.98 to -10.51; z = 4.32; P < .05). CONCLUSIONS: Meta-analysis of RCTs comparing tacks to suture fixation in LVHR showed comparable results with regard to postoperative chronic pain incidence and PI, and hernia recurrence. However, the operative time is shorter with tacks compared to suture fixation technique.
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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 | 0.015 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.039 |
| Bibliometrics | 0.007 | 0.007 |
| 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".