A332 ERCP COMPETENCY ASSESSMENT TOOL: DEVELOPMENT OF A PROCEDURE-SPECIFIC ASSESSMENT TOOL FOR ERCP.
Bibliographic record
Abstract
There is a paucity of literature evaluating competence in advanced endoscopic modalities in the treatment of pancreatobiliary diseases. Traditionally, competency in endoscopy has been measured by procedural volume and adverse events. A formal curriculum has not been widely established to standardize proficiency in ERCP. It is clear, from both training and patient quality perspectives that defined outcomes for clinical competence at the conclusion of advanced endoscopic training needs to be established. We propose to develop a competency tool for ERCP using a standardized Delphi Approach. Delphi Panel Recruitment and Sample: Delphi methodology will be used to reach consensus amongst a panel of recruited international therapeutic endoscopy experts (Therapeutic Endoscopists, Surgeons, Endoscopy Nurses) to establish criteria for assessing ERCP competence. Item Generation and Finalization of ERCP Competency Assessment Tool: Systematic Literature review (ERCP Performance and Assessment) followed by open-ended survey of panel members with opportunity for expert panelist commentary and input. Structure surveys distributed according to Dilman’s tailored design method. a) Respondents identify the importance of each checklist and global rating item using an ordinal scale. b) Respondents asked to list up to 10-additional checklist or global rating items not included they deeme to be important. Checklist Items (Figure 2) Divided into Pre-Procedure, Intra-Procedure and Post-Procedure domains. Procedure competency is further subdivided into Technical, Cognitive and Global Rating domains. Descriptive statistics to establish mean rating with a 95% confidence interval within each category of ordinal scoring. Checklist or global item to be removed if mean rating < 4.0 and critical rating <5.0. Consensus to be established if homogeneity of opinion seen in 80% or more of respondents in any Delphi round following the introductory round. No research to date has used trained raters using specifically designed assessment tools with direct observation or video recorded assessments. Rigorous continued assessment of a trainee’s performance with a validated assessment tool is valuable, providing insight to individual learning curves in comparison to other learners in a standardized fashion. Delphi panel results to be complete by CDDW/GRIT. CAG
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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 | 0.027 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".