Measuring Quality of Care in Patients With Nonvariceal Upper Gastrointestinal Hemorrhage: Development of an Explicit Quality Indicator Set
Bibliographic record
Abstract
OBJECTIVES: With an increasing emphasis on quality in health care and recognition of inconsistencies in the management of patients with nonvariceal upper gastrointestinal hemorrhage (NVUGIH), it is critical to establish a set of explicit quality indicators (QIs) in NVUGIH. METHODS: We conducted a nine-member, multidisciplinary expert panel and followed modified Delphi methods to systematically identify a set of QIs for NVUGIH. The panel performed independent ratings of each candidate QI using a nine-point RAND appropriateness scale, then met in person and re-voted using an iterative process of discussion. The final set comprised QIs with a median RAND Appropriateness Score >or=7 and no disagreement among experts. RESULTS: Among 116 candidate QIs, the panel rated 26 as valid measures of quality care. The selected QIs cover pre-endoscopy, endoscopy, and post-endoscopy care, including diagnosis, early resuscitation, risk stratification, endoscopic care, Helicobacter pylori management, and proton pump inhibitor therapy. CONCLUSIONS: We have developed an explicit set of evidence-based QIs in NVUGIH, providing physicians and institutions with a tool to identify processes amenable to quality improvement. This tool is intended to be applicable in all institutions providing care for NVUGIH patients.
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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 | 0.124 | 0.251 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".