Quality Indicator Development for the Approach to Ineffective Esophageal Motility
Bibliographic record
Abstract
GOALS: Develop quality indicators for ineffective esophageal motility (IEM). BACKGROUND: IEM is identified in up to 20% of patients undergoing esophageal high-resolution manometry (HRM) based on the Chicago Classification. The clinical significance of this pattern is not established and management remains challenging. STUDY: Using RAND/University of California, Los Angeles Appropriateness Methods, we employed a modified-Delphi approach for quality indicator statement development. Quality indicators were proposed based on prior literature. Experts independently and blindly scored proposed quality statements on importance, scientific acceptability, usability, and feasibility in a 3-round iterative process. RESULTS: All 10 of the invited esophageal experts in the management of esophageal diseases invited to participate rated 12 proposed quality indicator statements. In round 1, 7 quality indicators were rated with mixed agreement, on the majority of categories. Statements were modified based on panel suggestion, modified further following round 2's virtual discussion, and in round 3 voting identified 2 quality indicators with comprehensive agreement, 4 with partial agreement, and 1 without any agreement. The panel agreed on the concept of determining if IEM is clinically relevant to the patient's presentation and managing gastroesophageal reflux disease rather than the IEM pattern; they disagreed in all 4 domains on the use of promotility agents in IEM; and had mixed agreement on the value of a finding of IEM during anti-reflux surgical planning. CONCLUSION: Using a robust methodology, 2 IEM quality indicators were identified. These quality indicators can track performance when physicians identify this manometric pattern on HRM. This study further highlights the challenges met with IEM and the need for additional research to better understand the clinical importance of this manometric pattern.
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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 | 0.291 | 0.421 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".