Shouldice vs Lichtenstein Inguinal Hernia Repair: A Propensity Score–Matched Analysis Comparing Early Postoperative Pain and Quality of Life
Bibliographic record
Abstract
BACKGROUND: Tissue-based inguinal hernia repairs (IHRs), such as the Shouldice technique, are an acceptable alternative to mesh-based repair in appropriately selected patients; however, they are often thought to be more painful than tension-free repair in the early postoperative period. We aimed to compare short-term pain and quality of life (QoL) between the Shouldice and Lichtenstein techniques for IHR. STUDY DESIGN: The Abdominal Core Health Quality Collaborative database was queried for patients who underwent open IHR between January 2017 and April 2023. Patients who underwent Shouldice repair were propensity score matched to patients who underwent Lichtenstein repair based on several covariates: age, BMI, American Society of Anesthesiologists class, diabetes, anticoagulation use, smoking, scrotal component, and QoL overall score at baseline. The primary outcome was 30-day QoL as measured by the validated European Registry for Abdominal Wall Hernias (EuraHS) survey with overall, pain, and restriction-specific subdomains where higher scores are associated with worse QoL. Additionally, the amount of narcotics taken was evaluated. RESULTS: A total of 1,600 patients underwent IHR by either Shouldice or Lichtenstein technique. After propensity score matching, 109 patients remained in the Shouldice group, and 183 patients remained in the Lichtenstein group. At 30 days, median overall (13.33 vs 14, p = 0.5), pain (3 vs 2, p = 0.12), and restriction (6 vs 4, p = 0.4) EuraHS scores were similar between the Shouldice and Lichtenstein groups. Patients who underwent Shouldice repair were more likely to have taken zero opioid tablets than those who underwent Lichtenstein repair (68% vs 55%, p = 0.035). CONCLUSIONS: Shouldice repair demonstrated similar pain and QoL at 30 days when compared with Lichtenstein repair. These data should guide shared decision-making discussions when considering which approach to perform in suitable candidates undergoing open IHR.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".