Look Beyond the Disease Activity Score of 28 Joints (DAS28): Tender Points Influence the DAS28 in Patients with Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To explore the influence of tender points (TP) on the Disease Activity Score assessing 28 joints (DAS28) in patients with rheumatoid arthritis (RA). METHODS: In 200 consecutive patients with RA from the outpatient clinic, DAS28 and its components, tender and swollen joint counts (TJC, SJC, respectively), visual analog scale (VAS) for patient's general health (GH), and erythrocyte sedimentation rate (ESR), along with a tender point count (TPC) were assessed. Patients were categorized according to 4 TPC classes: zero, 1-5, 6-10, and ≥ 11 TP. The influence of TPC classes on DAS28 and its individual components was determined with Kruskal-Wallis tests and correlations between TP and DAS28 and its components were calculated. RESULTS: In 196 eligible patients, 70% were female, mean age was 59 years, and median disease duration was 3.9 years; median DAS28 was 3.1; and 49% had active disease, defined as DAS28 > 3.2. In 15% of patients, the TPC was ≥ 11, in 12% 6-10, in 30% 1-5, and in 43% zero. TPC significantly influenced the DAS28 and its less objective components TJC and VAS-GH (i.e., based on patient's report), but not the more objective DAS28 components SJC and ESR (i.e., observer- and laboratory-based). CONCLUSION: DAS28 is influenced by tender points, even in the non-fibromyalgia range, falsely suggesting higher disease activity and decreasing the sensitivity of the DAS28 criterion of low disease activity or remission. When applying DAS28-guided "tight control" or "treat-to-target" treatment strategies in RA, evaluation of not only the DAS28, but also its individual components along with a full joint and physical evaluation including assessment of TP is required to reliably estimate the individual's disease activity, which guides therapeutic 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".