Is Desarda technique suitable to emergency inguinal hernia surgery? A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Despite the fact that Lichtenstein is the gold standard for uncomplicated inguinal hernia, the use of mesh in an emergency context remains controversial. Pure tissue repairs have an essential role in the management of incarcerated or strangulated inguinal hernia. To date, there has been little agreement on what is the best surgical technique suitable for emergency hernia surgery. This systematic review aims to evaluate the efficacy and safety of the pure tissue Desarda technique for emergency inguinal hernia repair. METHODS: A complete search of electronic databases including PubMed/Medline, Web of Science, Embase and, Cochrane library was realized. Newcastle-Ottawa-Scale (NOS) (selection and outcome criteria) was used for quality assessment of included studies. The pooled prevalence of post-operative complications (surgical site infection, hematoma/seroma, chronic pain and, recurrence rate) was estimated. RESULTS: We included 5 studies from different countries. There were 2 randomized controlled trial and 3 observational cohort studies. Totally, there were 199 patients with a mean age of 57.6 years. Male patients were predominant (n = 196). The pooled prevalence of surgical site infection and hematoma/seroma was respectively 16.56% (95% CI: 11.74-22.39) and 12.43% (95%CI: 6.90-20.108). The pooled prevalence of chronic pain and recurrence was respectively 4.35% (95% CI: 1.04-11.47) and 2.10% (95%CI: 0.61-5.14). CONCLUSIONS: In summary, Desarda technique is feasible in emergency context with good results. We found any particularly important rate of complications considering the surgery in emergency context. Further studies should be realized to raise the level of evidence.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.028 |
| Bibliometrics | 0.006 | 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) | 0.004 | 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".