The learning curve for the Shouldice Repair: a pilot analysis of post-training specialized surgeons at the Shouldice Hospital
Bibliographic record
Abstract
PURPOSE: The aim of the study was to evaluate operative time and postoperative complications of 4 post-training specialized surgeons. METHODS: This was a pilot retrospective chart review to determine the learning curve of a Shouldice primary inguinal hernia repair (Shouldice Repair) of 4 post-training specialized surgeons, at the Shouldice Hospital. The first 300 Shouldice Repairs (early learning block) were compared to their 900-1,000 repairs as the primary operating surgeon (late learning block). Data was collected from the hospital's database. The learning curve was examined using cumulative sum analysis (CUSUM). RESULTS: During the early learning block cases, the surgeons had a mean operating time of 59.2 ± 11.2 min. The late learning block cases had significantly reduced operative time (53.4 ± 10.5 min, p = 0.001). According to the CUSUM analysis all four surgeons had a plateau after 78 to 88 operations in terms of operative time. A nonsignificant reduction in the rate of reported recurrences (n = 16 vs. n = 0) and surgical site occurrences (haematoma, seroma, infection; n = 27 vs. n = 2) was found between the early and late learning block cases. CONCLUSION: The operating time plateaued after 78-88 Shouldice Repairs for the 4 surgeons trained and working at the Shouldice Hospital. A nonsignificant trend towards fewer complications were noted among late learning block cases.
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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 | 0.004 |
| 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) | 0.002 | 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".