Improvements in Fatigue Lag Behind Disease Remission in Early Rheumatoid Arthritis: Results From the Canadian Early Arthritis Cohort
Bibliographic record
Abstract
OBJECTIVE: To examine the relationship between disease activity and fatigue over time in early rheumatoid arthritis (RA). METHODS: Data were from patients with early RA (duration of symptoms ≤12 months) enrolled in the Canadian Early Arthritis Cohort (CATCH). Patients rated their fatigue over the past week using an 11-point numerical rating scale (NRS) for up to 5 years of follow-up. Fatigue severity was classified as low (≤2), moderate (>2 but <5), or high (≥5). Differences in fatigue ratings in patients who achieved a low disease state (Disease Activity Score in 28 joints [DAS28] <3.2) and those who did not within 3-months of cohort entry were compared. RESULTS: Of 1,864 patients included, 88% met RA criteria, and 72% were women. The mean ± SD baseline DAS28 was 4.9 ± 1.5. Nineteen percent of the patients reported moderate baseline fatigue, and 59% reported severe baseline fatigue. Fatigue was correlated with pain and patient global ratings (r = 0.56-0.67, P < 0.001), and was weakly correlated with DAS28, tender joint count, swollen joint count, physician global assessment of disease activity, erythrocyte sedimentation rate, and C-reactive protein level. Patients who reported low fatigue by 3 months had significantly lower fatigue throughout follow-up compared to those who had moderate or high fatigue at 3 months (P < 0.001). Patients who achieved a DAS28 <3.2 within 3 months had significantly lower fatigue ratings (mean ± SD 2.7 ± 2.6) than those with a DAS28 >3.2 (4.6 ± 3.0) (P < 0.001), with improvements in fatigue that persisted through 5 years of follow-up. Maximal improvements in fatigue lagged behind remission by 6 months. CONCLUSION: Fatigue is common in early RA, and improvements may occur after remission. Early treatment response within 3-months was associated with short-term and long-term benefits in fatigue over time.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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".