The Longitudinal Course of Fatigue in Antineutrophil Cytoplasmic Antibody–associated Vasculitis
Bibliographic record
Abstract
OBJECTIVE: Fatigue is common and burdensome in antineutrophil cytoplasmic antibody-associated vasculitis (AAV). This study aimed to understand how fatigue changes over time following treatment initiation and to determine whether individuals with the poorest prognosis can be robustly identified. METHODS: One hundred forty-nine patients with AAV and new-onset disease recruited to 2 clinical trials (RITUXVAS and MYCYC) were followed for 18 months. Fatigue was measured at baseline and 6-month intervals using the vitality domain of the Medical Outcomes Study Short Form-36 quality of life questionnaire and compared to a cohort of 470 controls. Group-based trajectory modeling (GBTM) determined trajectories of the symptom to which baseline characteristics and ongoing fatigue scores were compared. RESULTS: Fatigue levels at diagnosis were worse in patients than controls [median (interquartile range; IQR) 30 (10-48) vs 70 (55-80); p < 0.001], with 46% of patients reporting severe fatigue. Fatigue improved after 6 months of treatment but remained worse than in controls (p < 0.001). GBTM revealed varied trajectories of fatigue: low fatigue stable (n = 23), moderate baseline fatigue improvers (n = 29), high baseline fatigue improvers (n = 61), and stable baseline high fatigue (n = 37). Participants who followed stable high fatigue trajectories had lower vasculitis activity compared to improvers, but no other demographic or clinical variables differed. CONCLUSION: This study longitudinally measured fatigue levels in patients with AAV. Although most patients improved following treatment, an important subgroup of patients reported persistently high levels of fatigue that did not change. Few clinical or laboratory markers distinguished these patients, suggesting alternative interventions specific for fatigue are required. [clinicaltrialsregister.eu, RITUXVAS EudraCT number: 2005-003610-15; MYCYC EudraCT number: 2006-001663-33].
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".