Determining the Minimally Important Difference in the Clinical Disease Activity Index for Improvement and Worsening in Early Rheumatoid Arthritis Patients
Bibliographic record
Abstract
OBJECTIVE: Simplified measures to quantify rheumatoid arthritis (RA) disease activity are increasingly used. The minimum clinically important differences (MCID) for some measures, such as the Clinical Disease Activity Index (CDAI), have not been well-defined in real-world clinic settings, especially for early RA patients with low/moderate disease activity. METHODS: Data from Canadian Early Arthritis Cohort patients were used to examine absolute change in CDAI in the first year after enrollment, stratified by disease activity. MCID cut points were derived to optimize the sum of sensitivity and specificity versus the gold standard of patient self-reported improvement or worsening. Sensitivity, specificity, positive predictive values, and negative predictive values were calculated against patient self-reported improvement (gold standard) and for change in pain, Health Assessment Questionnaire (HAQ), and Disease Activity Score in 28 joints (DAS28) improvement. Discrimination was examined using the area under receiver operator curves. Similar methods were used to evaluate MCIDs for worsening for patients who achieved low disease activity. RESULTS: A total of 578 patients (mean ± SD age 54.1 ± 15.3 years, 75% women, median [interquartile range] disease duration 5.3 [3.3, 8.0] months) contributed 1,169 visit pairs to the improvement analysis. The MCID cut points for improvement were 12 (patients starting in high disease activity: CDAI >22), 6 (moderate: CDAI 10-22), and 1 (low disease activity: CDAI <10). Performance characteristics were acceptable using these cut points for pain, HAQ, and DAS28. The MCID for CDAI worsening among patients who achieved low disease activity was 2 units. CONCLUSION: These minimum important absolute differences in CDAI can be used to evaluate improvement and worsening and increase the utility of CDAI in clinical practice.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".