Competency Assessment for Laparoscopic Anti-Reflux Surgery: Design and Delphi Review, a Collaboration With the American Foregut Society
Bibliographic record
Abstract
Background: Laparoscopic anti-reflux surgery, including hiatus hernia repair, is a common operation performed by both general and thoracic surgeons and an important learning objective for surgical trainees. This study aimed to design a competency assessment instrument for laparoscopic anti-reflux surgery. Method: A comprehensive competency assessment instrument was designed by a process of logical analysis by four expert thoracic surgeons with an interest in foregut surgery, and then reviewed informally by a panel of experts. The instrument was then further assessed and refined using a modified Delphi process. The Delphi questionnaire was distributed to all members of the Fellowship Training Committee of the American Foregut Society (n=21). Results: A first draft of the competency assessment instrument included 32 steps in four categories. The first round of the Delphi review was completed by 14 respondents (response rate 66.7%). A total of three rounds of Delphi review were performed. Ultimately, 25 items were retained from the original instrument and one modified and four new items were added. The final instrument has 30 steps in four categories. Conclusions: An international and inter-specialty consensus was established on the key components of assessing competence to perform anti-reflux surgery. The resulting instrument could be used to guide competency based assessments of general and thoracic surgeons and trainees.
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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 | 0.164 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".