PWE-418 Endoscopy training in the uk; the joint advisory group on gastrointestinal endoscopy national survey
Bibliographic record
Abstract
Introduction Gastrointestinal (GI) endoscopy is an important skill for both gastroenterologists and general surgeons but concerns have been raised about the provision and delivery of training. This survey aimed to evaluate and compare the delivery of endoscopy training to gastroenterology and surgical trainees within the UK. Method A nationwide electronic survey of UK gastroenterology and general surgical trainees. Results There were 216 responses (33% gastroenterology, 67% surgical). Gastroenterology trainees attended more endoscopy lists (mean 3.0 vs. 1.2) and training lists than surgical trainees (0.9 vs. 0.5). A significantly higher proportion of gastroenterologists than surgeons had already achieved accreditation in gastroscopy (60.8% vs. 28.9%), colonoscopy (66.7% vs. 1.4%) and flexible sigmoidoscopy (33.3% vs. 3.0%). More gastroenterology trainees than surgical trainees aspired to achieve accreditation in gastroscopy (97.2% vs. 79.2%), flexible sigmoidoscopy (91.7% vs. 70.1%) and colonoscopy (88.8% vs. 55.5%) by completion of training. By completion of training, surgeons were less likely than gastroenterologists to have completed the required number of procedures to gain accreditation in gastroscopy (60.3% vs. 91.3%), flexible sigmoidoscopy (64.6% vs. 68.6%) and colonoscopy (60.3% vs. 70.3%). Conclusion This survey highlights marked disparities between surgical and gastroenterology trainees in both aiming for and achieving accreditation in endoscopy. Without changes to the delivery and provision of training, as well as clarification of the role of endoscopy training within a surgical training programme, future surgeons will not be able to perform essential endoscopic assessment of patients as part of their management algorithm. Disclosure of interest None Declared.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".