Patient-derived Joint Counts Are a Potential Alternative for Determining Disease Activity Score
Bibliographic record
Abstract
OBJECTIVE: To investigate the correlation between the Disease Activity Score using a 28-joint count (DAS28) based on physician-derived joint counts and the DAS28 based on patient-derived joint counts (Pt-DAS28) in rheumatoid arthritis (RA). METHODS: Data from a multicenter, open-label study investigating the immunogenicity of etanercept (ETN) were analyzed. ETN-naive patients with active RA received ETN 50 mg once weekly alone or with methotrexate (MTX). Joint counts were performed at baseline, Week 12, and Week 24 by the physician and patient independently. Patients received instruction in performing joint assessments. RESULTS: Of 447 patients enrolled (ETN, n = 218; ETN + MTX, n = 229), most were women (79%) and the mean age was 54.5 years. Correlation coefficients between DAS28 and Pt-DAS28 were > or = 0.57 at baseline, Week 12, and Week 24. At Week 24, 48%, 39%, and 12% of patients could be classified as having low, moderate, or high disease activity, respectively, using DAS28. Using Pt-DAS28, 43%, 39%, and 18% were similarly classified. Agreement in the category of disease activity classification occurred in 72% of patients (kappa = 0.55). At Week 24, 78% of patients using DAS28 and 72% of patients using Pt-DAS28 were classified as moderate or good European League Against Rheumatism responders. CONCLUSION: These results support the possible use of patient-derived tender and swollen joint counts to aid in the assessment of disease activity and clinical response in patients with RA.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".