Advanced Endoscopy Trainee Involvement Early in EUS Training May Be Associated with an Increased Risk of Adverse Events
Bibliographic record
Abstract
Abstract Background The quality of endoscopic ultrasound (EUS) involving advanced endoscopy trainees (AETs) is not well understood. In this study, we aimed to examine adverse events (AE) risk and diagnostic yield of EUS procedures involving AETs. Methods We conducted a retrospective single-centre review from September 2009 to August 2015. Clinical, procedural, cytological, and hospital visit data within 30 days of the EUS procedure was collected. Primary outcomes were occurrence of an AE and a diagnostic specimen on cytopathology. Each AE was classified as “definitely related,” “possibly related,” or “not related” to the EUS procedure based on a previously defined consensus approach. Advanced endoscopy trainee involvement was established through the operative report. Results Our study included 1657 EUS procedures, of which 27% (451 of 1657) involved AETs. Endoscopic ultrasound was most commonly performed to evaluate pancreatic pathology (46% of cases). Overall AE incidence was 3.4%; it was 4.9% when an AET was involved and 2.8% when the EUS was performed without an AET (P = 0.04). The risk of an AE when AETs were involved was greatest in the first three months of training (7.9% versus 2.7%, P = 0.04). Multivariate analysis limited to the first three months of training demonstrated AET involvement to be associated with an increased AE risk after adjusting for patient and procedural factors (adjusted OR 3.2; 95% CI, 1.1–8.7; P = 0.03). The overall diagnostic yield was 76%. This was not compromised by AET involvement for any quartile of training. Conclusions We observed an increased risk of EUS-related AEs when procedures involved AETs during the first three months of training.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".