Safety and effectiveness of laparoscopic adult groin hernia repair in Africa: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Surgery is the recommended treatment of groin hernia, and laparoscopic approach is increasingly accepted due to lower risk of chronic pain. This systematic review aims to evaluate results of laparoscopic groin hernia repair (LGHR) in Africa. METHODS: We performed a literature search of published studies using electronic databases. Included African articles reported at least one of outcomes after LGHR in adult population. Newcastle-Ottawa Scale was used for quality assessment. A quantitative meta-analysis was performed to estimate the pooled prevalence of the post-operative outcomes. RESULTS: We included 19 studies from 6 countries which totalized 2329 hernia cases. Mean age was 44.5 years and male patients were predominant (sex-ratio 19.8). The mean operative time was 69.1 min. The pooled prevalence of conversion to open procedure was 2.578% (95% IC: 1.209-4.443). The pooled prevalence of surgical site infection and Hematoma/Seroma was respectively 0.626% (95%IC: 0.332-1.071) and 4.617% (95% IC: 2.990-6.577). The pooled prevalence of recurrence and chronic pain was respectively 2.410% (95% IC: 1.334-3.792) and 3.180% (95% IC: 1.435-5.580). We found that total morbidity for TAPP procedure was higher than TEP procedure (p = 0.0006; OR 1.8443). CONCLUSION: These results confirm that LGHR is safe and feasible and would be recommended in our African context.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.030 |
| Bibliometrics | 0.006 | 0.006 |
| 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".