2019 American College of Rheumatology Recommended Patient‐Reported Functional Status Assessment Measures in Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To develop American College of Rheumatology (ACR) recommendations for patient-reported Functional Status Assessment Measures (FSAMs) for use in routine clinical practice in patients with rheumatoid arthritis (RA). METHODS: We convened a workgroup to conduct a systematic review of published literature through March 16, 2017 and abstract FSAM properties. Based upon initial search results and clinical input, we focused on the following FSAMs appropriate for routine clinical use: the Health Assessment Questionnaire (HAQ) and derived measures and the Patient-Reported Outcomes Measurement Information System (PROMIS) tool. We used the Consensus-Based Standards for the Selection of Health Measurement Instruments (COSMIN) 4-point scoring method to evaluate each FSAM, allowing for overall level of evidence assessment. We identified FSAMs fulfilling a predefined minimum standard and, through a modified Delphi process, selected preferred FSAMs for regular use in most clinic settings. RESULTS: The search identified 11,835 articles, of which 56 were included in the review. Descriptions of the measures, properties, study quality, level of evidence, and feasibility were abstracted and scored. Following a modified Delphi process, 7 measures fulfilled the minimum standard for regular use in most clinic settings, and 3 measures were recommended: the PROMIS physical function 10-item short form (PROMIS PF10a), the HAQ-II, and the Multidimensional HAQ. CONCLUSION: This work establishes ACR recommendations for preferred RA FSAMs for regular use in most clinic settings. These results will inform clinical practice and can support future ACR quality measure development as well as highlight ongoing research needs.
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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 | 0.041 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".