Standard reporting elements for the performance of EUS: Recommendations from the FOCUS working group
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Quality indicators for the performance of EUS have been developed to monitor and improve service value and patient outcomes. To support the incorporation of these indicators and standardize EUS documentation, we propose standard EUS reporting elements for endosonographers and endoscopy units. METHODS: A comprehensive literature search and review was performed to identify EUS quality indicators and key components of high-quality standardized EUS reporting. Guidance statements regarding standard EUS reporting elements were developed and reviewed at the Forum for Canadian Endoscopic Ultrasound (FOCUS) 2019 Annual Meeting. RESULTS: EUS reporting elements can be divided into preprocedural, intraprocedural, and postprocedural items. Preprocedural components include the type, indication, and urgency of the procedure and patient clinical information and consent. Intraprocedural components include the adequacy and extent of examination, relevant landmarks, lesion characteristics, sampling method, specimen quality, and intraprocedural adverse events. Postprocedural components include a summary and synthesis of relevant findings as well as recommended management and follow-up. CONCLUSIONS: Standardizing reporting elements may help improve the care of patients undergoing EUS procedures. Our review provides a practical guide and compilation of recommended reporting elements to ensure ongoing best practices and quality improvement in EUS.
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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 | 0.382 | 0.461 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.011 |
| Bibliometrics | 0.025 | 0.016 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.017 | 0.009 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".