Learning curve for laparoscopic totally extraperitoneal repair of inguinal hernia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Laparoscopic totally extraperitoneal (TEP) repair has been accepted as a popular procedure for inguinal hernia repair, but surgeons still encounter technical difficulties owing to unfamiliar pelvic anatomy and limited working space. We sought to estimate the learning curve for laparoscopic TEP repair without supervision. METHODS: We retrospectively analyzed the medical records of patients scheduled for laparoscopic TEP repair of an inguinal hernia from December 2000 to October 2007. RESULTS: We reviewed medical records for 700 patients. The cases were divided into 8 groups: 20 patients each in groups I-V and 200 patients each in groups VI-VIII. No significant difference in demographic characteristics was identified among the groups. The mean duration of surgery significantly decreased (p < 0.001) in relation to experience; it reached a plateau of less than 30 minutes (mean 28 min) after 60 cases. The mean length of stay in hospital was 0.97 days, reaching a plateau after 20 cases. Six patients were converted to other techniques: 1 patient each in groups III and VIII and 4 patients in group VII. Three recurrences were detected; however, 2 were excluded because the patient had bilateral inguinal hernias. CONCLUSION: We estimate the learning curve for laparoscopic TEP repair is 60 cases for a beginner surgeon. The presence of an experienced supervisor during the first 60 cases can help prevent unnecessary complications and shorten the duration of surgery.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it