Shouldice versus Lichtenstein inguinal hernia repair: A meta‐analysis of randomized controlled trials
Bibliographic record
Abstract
Abstract Background The Lichtenstein technique is the standard treatment for adult open inguinal hernia repair. Among the non‐mesh repair techniques, Shouldice has shown the best results and is comparable to mesh repairs in selected cases. Due to the risk of chronic groin pain associated with the Lichtenstein technique, Shouldice has increased in popularity, and some surgeons have adopted it as a viable first‐line option. Methods MEDLINE, Cochrane, Central Register of Clinical Trials, and EMBASE for randomized controlled trials (RCT) published until February 2024. Risk ratios (RRs) with 95% confidence intervals (CIs) were pooled using a random‐effects model. Heterogeneity was assessed using the Cochran Q test and I 2 statistics with p ‐values <0.10 and I 2 > 25% considered significant. Statistical analysis was performed using R Software, version 4.1.2. Results Fourteen RCTs comprising 2784 patients were included, of whom 1379 (47.5%) were submitted to the Shouldice hernia repair and 1513 (52.5%) to the Lichtenstein technique. Shouldice was associated with a significant increase in the recurrence rate (4.2% vs. 0.9%; RR 3.68; 95% CI 2.05–6.60; p < 0.001; I 2 = 0%) compared with Lichtenstein. The number needed to treat (NNT) to prevent one Shouldice recurrence was 30.3. There were no significant differences between groups in chronic pain, urinary retention, bladder injury, testicular atrophy, wound infection, hematoma–seroma, or hypesthesia. Conclusion The Lichtenstein technique was associated with reduced recurrence rates compared with Shouldice in patients undergoing inguinal hernia repair. However, the overall recurrence rate with the Shouldice technique was still low (4.2%), suggesting that it may be a viable option in selected patients.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.034 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".