The learning curve for the Shouldice Repair: a pilot analysis of post-training specialized surgeons at the Shouldice Hospital
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
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.
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.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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