Update to a Systematic Review on Quality Measures for Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: This work aims to provide an update on a previously published systematic review on quality measures (QMs) in rheumatoid arthritis (RA) to inform future measure development and endorsement efforts. METHODS: We searched published and grey literature sources from January 2018 to July 2023. Included sources were limited to those that targeted patients with RA, either exclusively or alongside other rheumatic conditions, and that were guided by a clear consensus-building methodology. We extracted QMs and categorized them into topic and subtopics. Extracted measures were then reviewed for similarity with the previously extracted set from a published systematic review. Nonduplicative measures were labeled as "new." RESULTS: The updated search resulted in 2395 citations, from which 339 studies were selected for full-text review. From these, a total of 6 studies met the inclusion/exclusion criteria, resulting in the extraction of 156 QMs, along with 31 additional QMs extracted from 87 grey literature sources. Among the 187 extracted QMs, most were duplicative and/or similar to previously developed measures (68%, n = 127). Sixty were identified as new. New QMs were primarily structural (50%, n = 30) and process measures (33%, n = 20). New QMs frequently addressed the topic of healthcare delivery (35%, n = 21). CONCLUSION: Although this review identified new QMs that reflect various reassuring trends in healthcare quality assessment, most were duplicative or similar to existing measures. This highlights the need for a centralized way to reduce redundancy in QM development efforts and to enable easy dissemination of QMs to optimize care for individuals with RA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.089 | 0.336 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.013 |
| Bibliometrics | 0.032 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".