Recurrence Rates after Primary Femoral Hernia Open Repair a Systematic Review
Bibliographic record
Abstract
PURPOSE: Femoral hernia accounts for 22% of groin hernia operations in women and for 1.1% in men. Numerous surgical approaches have been reported but there is no consensus. Many of the recurrence rates are reported in old literature, while recent reports are scarce. The aim of the present study was to review rates of recurrences in patients who underwent open repair of a primary femoral hernia. METHODS: We conducted a systematic search in the electronic literature, using the search terms "femoral hernia" and "recurrence". We included studies published from 2002 that had as primary or secondary endpoint to evaluate the recurrence after surgery. Risk of bias was assessed by the Cochrane risk of bias tool for RCT and by the Newcastle-Ottawa Scale for cohort studies. RESULTS: Fifteen eligible articles were included in our systematic review. A total of 1087 procedures were performed according to the defined criteria. The metanalytic evaluation highlighted a higher probability of recurrence for non-mesh than mesh repairs (6.5% vs 1.9%; RR 0.924, 95% CI: 0.857 - 0.996). In patients treated in emergency settings the rate of recurrences was 3.7%; in patients who received elective repairs it was 0.71%. Six studies reported that most of recurrences occurred within the first post-operative year. CONCLUSION: We found that crude recurrence rate after open repair of a primary femoral hernia is about 4%. This rate is higher in case of non-mesh techniques and in emergency surgery. Our results support the recommendation that femoral hernias should be repaired with mesh techniques.
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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 | 0.013 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.014 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".