Persistent neuropathic pain after inguinal herniorrhaphy depending on the procedure (open mesh v. laparoscopy): a propensity-matched analysis
Bibliographic record
Abstract
BACKGROUND: A greater incidence of persistent pain after inguinal herniorrhaphy is suspected with the open mesh procedure than with laparoscopy (transabdominal preperitoneal), but the involvement of neuropathy needs to be clarified. METHODS: We examined the cumulative incidence of neuropathic persistent pain, defined as self-report of pain at the surgical site with neuropathic aspects, within 6 months after surgery in 2 prospective subcohorts of a multicentre study. We compared open mesh with laparoscopy using different analysis, including a propensity-matched analysis with the propensity score built from a multivariable analysis using a generalized linear model. RESULTS: Considering the full patient sample (242 open mesh v. 126 laparoscopy), the raw odds ratio for neuropathic persistent pain after inguinal herniorrhaphy was 4.3. It reached 6.8 with the propensity-matched analysis conducted on pooled subgroups of 194 patients undergoing open mesh and 125 undergoing laparoscopy (95% confidence interval 1.5-30.4, p = 0.012). A risk factor analysis of these pooled subgroups revealed that history of peripheral neuropathy was an independent risk factor for persistent neuropathic pain, while older age was protective. CONCLUSION: We found a greater risk of persistent pain with open mesh than with laparoscopy that may be explained by direct or indirect lesion of nerve terminations. Strategies to identify and preserve nerve terminations with the open mesh procedure are needed.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".