Increasing Treatment in Early Rheumatoid Arthritis Is Not Determined by the Disease Activity Score But by Physician Global Assessment: Results from the CATCH Study
Bibliographic record
Abstract
OBJECTIVE: To determine the factors most strongly associated with an increase in therapy of early rheumatoid arthritis (ERA). METHODS: Data from the Canadian Early Arthritis Cohort (CATCH) were included if the patient had ≥ 2 visits and baseline and 6 months data. A regression analysis was done to determine factors associated with treatment intensification. RESULTS: Of 1145 patients with ERA, 790 met inclusion criteria; mean age was 53.4 years (SD 14.7), mean disease duration 6.1 months (SD 2.8), 75% were female, baseline Disease Activity Score-28 (DAS28) was 4.7 (SD 1.8) and 2.9 (SD 1.8) at 6 months for included patients. Univariate factors for intensifying treatment were physician global assessment (MDGA; OR 7.8 and OR 7.4 at 3 and 6 months, respectively, p < 0.0005), swollen joint count (SJC; OR 4.7 and OR 7.3 at 3 and 6 months, p < 0.0005), and DAS28 (OR 3.0 and OR 4.6 at 3 and 6 months, p < 0.0005). In the regression model only MDGA was strongly associated with treatment intensification (OR 1.5 and OR 1.2 at 3 and 6 months, p < 0.0005); DAS28 was not consistently predictive (OR 1.0, p = 0.987, and OR 1.2, p = 0.023, at 3 and 6 months). DAS28 was the reason for treatment intensification 2.3% of the time, compared to 51.7% for SJC, 49.9% for tender joint count, and 23.8% for MDGA. For the same SJC, larger joint involvement was more likely to influence treatment than small joints at 3 months (OR 1.4, p = 0.027). CONCLUSION: MDGA was strongly associated with an increase in treatment at 3 and 6 months in ERA, whereas DAS28 was not. Physicians rarely stated that DAS28 was the reason for increasing treatment.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".