The who, what, where, how, and why of endoscopic submucosal dissection in Canada: A survey among Canadian endoscopists
Bibliographic record
Abstract
BACKGROUND AND AIM: Endoscopic submucosal dissection (ESD) is an internationally accepted technique for the resection of superficial gastrointestinal neoplasia. ESD allows for en-bloc removal when endoscopic mucosal resection (EMR) is unsuitable due to the size or depth of the lesion. The aim of this survey was to examine Canadian clinicians' experience and perceptions of ESD as its prevalence increases across the country. METHODS: An electronic survey consisting of 24 multiple-choice questions was distributed via the Canadian Association of Gastroenterology email database and directly to those known to be performing or interested in ESD. The survey covered training, practice, obstacles in implementation, and perceptions of the future of ESD in Canada. RESULTS: A total of 21 participants completed the survey. ESD was performed primarily in the endoscopy suite exclusively (71%), and most operators (64%) performed it on an outpatient basis. Procedure time was selected as the greatest technical challenge in the performance of ESD by 86% of the participants. Both lack of formalized training and long procedure times were the highest ranked barriers to the adoption of ESD. Over the next 5 years, 95% believed there would be an increase in ESD volume in Canada, and 43% believed ESD was ready for adoption by more therapeutic endoscopists. INTERPRETATION: In this survey, we explored the current practice, attitude, and challenges of ESD in the Canadian landscape. As the performance of ESD increases and gains more acceptance across Canada, there are opportunities to address technical challenges and barriers through the formalization of training, education, and practice guidelines.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".