Predicting Low Disease State and Remission in Early Rheumatoid Arthritis in the First Six Months, Comparing the Simplified Disease Activity Index and European League Against Rheumatism Response Measures: Results From an Early Arthritis Cohort
Bibliographic record
Abstract
OBJECTIVE: To compare the European League Against Rheumatism (EULAR) and Simplified Disease Activity Index 50% (SDAI50) response measures (RMs) and their impact on future response to treatment in patients with early rheumatoid arthritis (ERA). METHODS: Biologic agent-naive ERA patients from the Canadian Early Arthritis Cohort study with complete data at baseline, 3, and 6 months were evaluated. Kappa statistics were used to evaluate the agreement between the EULAR (moderate or good response) and SDAI50 RMs. The RMs at 3 months were also compared for their ability to predict low disease activity state (LDAS) or remission (REM) at 6 months. RESULTS: A total of 1,124 patients were evaluated. Of those, 215 patients (30%) and 294 patients (45%) failed to achieve a EULAR and SDAI50 response, respectively. There was a good agreement between EULAR and SDAI50 RMs with a kappa of 0.59 (95% confidence interval 0.53-0.66). Throughout the range of disease activity, the SDAI50 response was shown to be more stringent than the EULAR response. Multivariable linear regression analysis demonstrated that both RMs at 3 months were associated with LDAS or REM at 6 months, and SDAI50 had a more significant impact on this outcome compared to the EULAR response. CONCLUSION: There is a good agreement between the EULAR and SDAI50 RMs. Although a minority of patients have discordant RMs at each end of the disease activity spectrum at baseline, the SDAI50 response at 3 months appears to be a more significant predictor of outcomes at 6 months than EULAR response.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".