A Systematic Review of Quality Measures for Inflammatory Arthritis
Bibliographic record
Abstract
OBJECTIVE: To conduct a systematic review and quality appraisal of quality measures for inflammatory arthritis, including rheumatoid arthritis (RA), spondyloarthritis, psoriatic arthritis (PsA), and juvenile idiopathic arthritis (JIA). METHODS: Embase, MEDLINE, and Cumulative Index to Nursing and Allied Health Literature (CINAHL) were searched from January 1, 2000, to October 23, 2016, using Medical Subject Headings terms for inflammatory arthritis and quality measures. A "grey literature" search of international arthritis organizations and quality measure libraries was also conducted. Two reviewers independently considered the papers for inclusion, with disagreements resolved by consensus. A modified guideline appraisal tool (AGREE II) was used to appraise the measure development process, which determined final inclusion. Measures were abstracted in duplicate and categorized into themes, measure type, and domains of quality. RESULTS: Thirteen measurement sets were included from 4 countries (United States, Canada, United Kingdom, Netherlands) and 1 European consortium. They included 10 sets on RA and 1 each for PsA, inflammatory arthritis, and JIA. There were 161 unique individual measures (136 process, 20 structure, and 5 outcome). Major themes included assessment, medications, and comorbidities. Measure development methods were varied, including RAND/University of California, Los Angeles appropriateness methodology, prioritization exercises, or other modified-Delphi methods. Inclusion of patients occurred in 77% of development groups. Discussion of barriers to measurement was infrequent. CONCLUSION: Inflammatory arthritis quality measures cover a diversity of themes encompassing process, structure, and outcomes of care across the 6 domains of quality. However, between organizations, measure development is not standardized. Local assessment of measurement feasibility before use outside the original development context is recommended.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".