Proper training in laparoscopic hernia repair is necessary to minimize the rising recurrence rate in Japan
Bibliographic record
Abstract
INTRODUCTION: The Japan Society for Endoscopic Surgery reported that the recurrence rate for inguinal hernia repair in Japan was less than 1% in 2010. However, its 2012 survey found that the recurrence rate had increased to 4% for the transabdominal preperitoneal procedure and 5% for the totally extraperitoneal procedure. We held 14 hernia repair training courses from 2011 to 2016 with help from Covidien. This study aimed to determine the effect of this training on the recurrence rate. METHODS: Training was composed of a theoretical revision of inguinal anatomy, dry laboratory suturing, a video lecture, and practice on an animal model. We made inquiries about the length of each surgeon's career, post-training changes in surgical methods, and recurrence rates before and after training. RESULTS: We received responses from 159 of 300 trainees (53%). The mean career length was 12.7 ± 8.2 years. The annual number of transabdominal preperitoneal procedures performed increased from 20.9 ± 29.9 to 32.4 ± 56.1 after training (P < 0.001), and the number of totally extraperitoneal procedures increased from 9.5 ± 13.9 to 13.9 ± 16.9 (P = 0.0218). The annual number of procedures performed via the anterior approach decreased from 153.1 ± 28.4 to 28.4 ± 52.2 after training (P < 0.001). The pre-training transabdominal preperitoneal procedure recurrence rate was 0.9%, and this decreased to 0.4% after training. There was no pre-training recurrence rate for the totally extraperitoneal procedure, but this was 0.4% after training. CONCLUSION: The high recurrence rate after inguinal hernia repair in Japan was mainly due to inadequate training in the laparoscopic method. Our laparoscopic hernia repair training course achieved low recurrence rates.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".