Development and usability of an endoscopist report card assessing ERCP quality
Bibliographic record
Abstract
BACKGROUND AND AIMS: Audit and feedback (A&F) for ERCP is relatively understudied despite the demonstrated effectiveness of A&F for endoscopic procedures such as colonoscopy. Endoscopist "report cards" are one such A&F tool. We aimed to develop an ERCP report card and assess its appropriateness, acceptability, and feasibility through usability testing. METHODS: A prototype report card was designed using a combination of published quality indicators and established predictors of adverse events (AEs). Exploratory analyses from a prospective multicenter registry were performed to further identify novel and/or understudied parameters for possible inclusion. Semistructured interviews with ERCP endoscopists were conducted and framework analysis performed. Validated postinterview usability instruments were administered. Feedback was incorporated to create a final report card. RESULTS: The report card included domains of technical parameters, AE rates and prevention, and patient-reported experience measures (PREMs). Qualitative feedback was positive, with respondents agreeing with inclusion of relevant content in most domains. Postinterview instruments revealed adequate appropriateness and acceptability. PREMs were believed by respondents to be poorly actionable and were replaced with appropriateness of indication and fluoroscopy usage parameters in the final report card. Concerns were raised regarding the feasibility of implementation because of reliance on difficult-to-obtain granular intraprocedural data. CONCLUSIONS: We designed and tested an ERCP report card that has the potential to be an effective A&F intervention for endoscopists in clinical practice. Although feasibility of data capture and implementation are currently limitations, advances in video recording and artificial intelligence technologies could accelerate widespread adoption of such a tool.
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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 | 0.074 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".