Learning curve for laparoscopic totally extraperitoneal repair of inguinal hernia
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".