Endoscopy services and training: a national survey of general surgeons
Bibliographic record
Abstract
BACKGROUND: Delivering high-quality endoscopy services depends largely on the competence of endoscopists. General surgery residency training in endoscopy and the associated quality of endoscopy services being delivered by general surgeons have been the subject of considerable controversy. In conjunction with the Canadian Association of General Surgeons (CAGS) executive board, we formulated a survey to evaluate the general state of endoscopy practice and training among general surgeons in Canada. METHODS: The study was designed as a cross-sectional survey. General surgeons who are members of CAGS were selected to participate in the study and were emailed a link to the online questionnaire regarding the importance of endoscopy. They were asked to compare their training to resident training today. RESULTS: Sixty-nine surveys were completed. The majority of general surgeons (95.7%) indicated that endoscopy was an important skill to possess, and more than 85.5% used endoscopy in their own practices. However, nearly half (46.4%) felt that general surgery endoscopy training in Canada is currently inadequate to produce competent endoscopists. The main qualitative themes emerging from the survey were the inadequacy of current postgraduate endoscopy training (37.5%) and the absence of standardization in training (25.0%). CONCLUSION: Endoscopy is considered integral to academic and community general surgeons' practices; however, the adequacy of training seems to be questioned. Postgraduate training in endoscopy needs to be formalized and standardized, with a greater emphasis placed on teaching endoscopy.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".