Development of American College of Rheumatology Quality Measures for Systemic Lupus Erythematosus: A Modified Delphi Process With Rheumatology Informatics System for Effectiveness (<scp>RISE</scp>) Registry Data Review
Bibliographic record
Abstract
OBJECTIVE: We aimed to develop readily measurable digital quality measure statements for clinical care in systemic lupus erythematosus (SLE) using a multistep process guided by consensus methods. METHODS: Using a modified Delphi process, an American College of Rheumatology (ACR) workgroup of SLE experts reviewed all North American and European guidelines from 2000 to 2020 on treatment, monitoring, and phenotyping of patients with lupus. Workgroup members extracted quality constructs from guidelines, rated these by importance and feasibility, and generated evidence-based quality measure statements. The ACR Rheumatology Informatics System for Effectiveness (RISE) Registry was queried for measurement data availability. In 3 consecutive Delphi sessions, a multidisciplinary Delphi panel voted on the importance and feasibility of each statement. Proposed measures with consensus on feasibility and importance were ranked to identify the top 3 measures. RESULTS: Review of guidelines and distillation of 57 quality constructs resulted in 15 quality measure statements. Among these, 5 met high consensus for importance and feasibility, including 2 on treatment and 3 on laboratory monitoring measures. The 3 highest-ranked statements were recommended for further measure specification as SLE digital quality measures: 1) hydroxychloroquine use, 2) limiting glucocorticoid use >7.5 mg/day to <6 months, and 3) end-organ monitoring of kidney function and urine protein excretion at least every 6 months. CONCLUSION: The Delphi process selected 3 quality measures for SLE care on hydroxychloroquine, glucocorticoid reduction, and kidney monitoring. Next, measures will undergo specification and validity testing in RISE and US rheumatology practices as the foundation for national implementation and use in quality improvement programs.
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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 | 0.481 | 0.434 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".