Early Pain and Surgical outcomes post laparoscopic ventral and incisional hernia repair.
Bibliographic record
Abstract
Introduction: Pain outcome post-Laparoscopic Ventral and Incisional Hernia Repair (LVIHR) has been largely attributed to the technique of mesh fixation. This study primarily aims to compare early postoperative pain and surgical outcomes with two fixation methods [absorbable tacks (AT) and non-absorbable tacks (NAT)], and two different types of meshes. Methods: This is a retrospective study of patients who underwent LVIHR between September 2011 and August 2016. The groups of mesh and fixation were compared with respect to early postoperative pain scores using Visual Analogue Score (VAS 0-10) and incidence of seroma, wound infection, and recurrence rates.Results: Fifty-six patients with LVIHR were enrolled (41 in AT and 15 in NAT group), PhysioMeshu2122 was used in 14 and Proceedu2122 mesh in 42 patients. Mean VAS was significantly higher in the AT as compared to NAT group at first 6 hours after operation (p=0.02), first 24 hours (p=0.04), day 2 (p=0.02), however, it was similar after 48 hours (p=0.2) and at 4 weeks postoperatively (0.8). There were no significant differences in postoperative pain between mesh groups. The fixation method had no effect on the incidence of seroma, wound infection and recurrence rate. However, seroma (p=0.02), wound infection (p=0.01) and hernia recurrence (p=0.01) were higher in PhysioMeshu2122 as compared to Proceed mesh group independent from methods of mesh fixation. Conclusion: AT mesh fixation was associated with higher postoperative pain intensity compared to NAT during the early postoperative period independent of mesh type, however, it was similar at 4 weeks. PhysioMeshu2122 was associated with higher recurrence rate, seroma and wound infection compared to Proceed mesh.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".