A222 TRAINEE INVOLVEMENT IN EUS PROCEDURES MAY INITIALLY BE ASSOCIATED WITH GREATER RISK AND LOWER DIAGNOSTIC YIELD: A LARGE SINGLE CENTRE STUDY
Bibliographic record
Abstract
Limited data exists on the quality of advanced endoscopic procedures completed by trainees. The aim of this study was to describe the quality of endoscopic ultrasound (EUS) procedures performed by advanced endoscopy trainees with staff supervision (TS), using diagnostic yield (DY) and adverse event risk (AE) as quality indicators. We performed a retrospective chart review of patients who underwent EUS at The Ottawa Hospital between September 2009 and May 2015. Data was collected regarding patient demographics, procedure details, and DYs. AEs were identified by reviewing all emergency room visits and hospitalizations within 30 days of the patient’s EUS procedure. Relation of the hospital encounter (definitely, possibly and not) to the EUS procedure was established by consensus using pre-defined criteria. 1647 EUS cases were analyzed. The median patient age was 64 (IQR, 53–73) years and 50% of the patients were male. The EUS DY was 78% and the risk of an AE was 3.5% (58 cases). 27% (450) of all EUS procedures were performed by TS. Overall, TS procedures were not associated with a reduction in DY (80%, p = 0.2) or excess AE risk (4.9%, p = 0.06) in comparison to procedures performed by a staff alone (SA). However, TS DY improved every 4 months (Table 1; 76%, 79%, 84%) and TS AE risk was highest in the first and last 4 months of training (Table 1; 6.8%, 2.1%, 5.9%). This trend was not seen for procedures performed by SA (see Table 1). In the first four months of training, EUS procedures performed by trainees may be associated with lower DY and increased risk of AEs. This warrants further evaluation to determine how to avoid compromising service quality during advanced endoscopy training. Table 1. Adverse event risk and diagnostic yield during the training period. *Chi-square or Fisher exact test as required comparing the SA and TS groups. SA= staff alone; TS = trainee with staff None
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".