The Association Between a Surgeon’s Learning Curve With Endovascular Aortic Aneurysm Repair and Previous Institutional Experience
Bibliographic record
Abstract
The purpose of the present study was to determine whether an institution's prior endovascular experience influenced the learning curve of subsequent surgeons. A prospective analysis of the initial 70 endovascular abdominal aortic aneurysm repair (EVAR) cases attempted by an individual surgeon was performed with the primary outcome variable being achievement and 30-day maintenance of initial clinical success. Along with standard statistical analyses, the cumulative sum failure method (CUSUM) was used to analyze the learning curve, with a predetermined acceptable failure rate of 10%. Seventy elective EVAR cases were performed by this surgeon during a 4-year period (2000-2004) (mean age, 73.7 -/+ 5.4 years; mean aneurysm diameter 63.3 -/+ 7.2 mm). Initial clinical success was achieved in 68 of 70 cases (97%), which differed significantly with that of our initial surgeon (88.5%, P = .01). Causes of failure in the present series included 1 early mortality (1.4%) and 1 case of conversion to open repair with no instances of type I endoleak or endograft limb thrombosis. Both surgeons' cases were plotted sequentially with CUSUM curves revealing a significantly shorter learning curve for the second surgeon. Optimal results were achieved following 10 to 20 EVAR cases, as opposed to 60 cases in the initial series. Such an analysis confirms that as an institution's experience with EVAR increases, an individual surgeon's learning curve shortens considerably.
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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 | 0.002 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".