Total Extraperitoneal Hernia Repair: Residency Teaching Program and Outcome Evaluation
Bibliographic record
Abstract
BACKGROUND: Total extraperitoneal (TEP) hernia repair has been shown to offer less pain, shorter postoperative hospital stay and earlier return to work when compared to open surgery. Our institution routinely performs TEP procedures for patients with primary or recurrent inguinal hernias. The aim of this study was to show that supervised senior residents can safely perform TEP repairs in a teaching setting. METHODS: All consecutive patients treated for inguinal hernias by laparoscopic approach from October 2008 to June 2012 were retrospectively analyzed from a prospective database. RESULTS: A total of 219 TEP repairs were performed on 171 patients: 123 unilateral and 48 bilateral. The mean patient age was 51.6 years with a standard deviation (SD) of ± 15.9. Supervised senior residents performed 171 (78 %) and staff surgeons 48 (22 %) TEP repairs, respectively. Thirty-day morbidity included cases of inguinal paresthesias (0.4 %, n = 1), umbilical hematomas (0.9 %, n = 2), superficial wound infections (0.9 %, n = 2), scrotal hematomas (2.7 %, n = 6), postoperative urinary retentions (2.7 %, n = 6), chronic pain syndromes (5 %, n = 11) and postoperative seromas (6.7 %, n = 14). Overall, complication rates were 18.7 % for staff surgeons and 19.3 % for residents (p = 0.83). For staff surgeons and residents, mean operative times for unilateral hernia repairs were 65 min (SD ± 18.9) and 77.6 min (SD ± 29.8) (p = 0.043), respectively, while mean operative times for bilateral repairs were 115 min (SD ± 40.1) and 103.6 (SD ± 25.9) (p = 0.05). CONCLUSIONS: TEP repair is a safe procedure when performed by supervised senior surgical trainees. Teaching of TEP should be routinely included in general surgery residency programs.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".