The Longitudinal Course of Fatigue in Rheumatoid Arthritis: Results from the Norfolk Arthritis Register
Bibliographic record
Abstract
OBJECTIVE: Fatigue is common and burdensome in rheumatoid arthritis (RA). Despite RA fatigue progression varying significantly between individuals in practice, existing longitudinal analyses only examine symptom advancement on a population level. This study aimed to determine fatigue trajectories at an individual level and to characterize those patients with the poorest prognosis, with a view to enabling earlier interventions. METHODS: Patients with RA reporting clinically relevant baseline fatigue (≥ 20 mm on a 0-100 mm visual analog scale) were identified from a longterm inflammatory polyarthritis cohort (the Norfolk Arthritis Register). Fatigue changes from baseline to 1- and 4-year followups were calculated, and sex-stratified group-based trajectory modeling (GBTM) determined trajectories of the symptom between which baseline characteristics were compared. RESULTS: Among 338 patients, only minimal average changes were observed between recruitment to 1 year (6.0 mm, SD 26.9) and 4 years (5.5 mm, SD 29.3). This was despite 45.6% and 40.7% of participants reporting clinically significant improvements (≥ 10 mm) at these respective followups. GBTM revealed varied trajectories of fatigue, which for both sexes consisted of Improved (men, n = 48 and women, n = 81) or persistent Moderate-high paths (n = 54, n = 105), and further included a persistent High trajectory in women (n = 50). Participants who followed persistent trajectories were best distinguished from improvers by patient-reported rather than demographic or clinical variables. CONCLUSION: Among patients with RA presenting with clinically relevant fatigue, distinct longitudinal symptom trajectories were identified on an individual level despite nominal average changes in fatigue on a group level. It is possible to identify and characterize subgroups of participants who report persistent fatigue and should therefore be targeted to receive future fatigue-alleviating interventions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".