The Uses of Disease Activity Scoring and the Physician Global Assessment of Disease Activity for Managing Rheumatoid Arthritis in Rheumatology Practice
Bibliographic record
Abstract
OBJECTIVE: To evaluate the uses of quantitative disease activity scoring and a physician global assessment of disease activity for managing rheumatoid arthritis (RA) in rheumatology practice. METHODS: The Global Arthritis Score (GAS) and a physician global assessment (Physician Global) were determined during each office visit for a community practice RA population. The GAS was calculated from patients' self-reported pain, functional assessment, and tender joint count. The Physician Global was recorded on a 10-point visual analog scale. The correlation of these 2 disease activity measures was determined for the most recent office visit of 185 patients with RA, and the reasons for discordant results were identified by chart review. RESULTS: The GAS and Physician Global were concordant for active or inactive disease in 126 of 185 patients (68%) and were discordant in 59 (32%). Forty-five of these discordant patients had a high GAS while their Physician Global indicated inactive disease. Their GAS values were high because of osteoarthritis, back pain, soft tissue rheumatism, and/or prior joint damage rather than active RA. The other 14 patients had a low GAS with an uncontrolled Physician Global for a variety of reasons. CONCLUSION: (1) An RA disease activity score and a quantitative Physician Global can be measured during rheumatology office visits to document patients' disease status. (2) Disease activity scoring contributes valuable information, but should not replace the Physician Global in guiding RA patient management or reimbursement decisions.
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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 | 0.015 | 0.102 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".