Fatigue in Ankylosing Spondylitis and Nonradiographic Axial Spondyloarthritis: Analysis from a Longitudinal Observation Cohort
Bibliographic record
Abstract
OBJECTIVE: In this study, we aimed to address the prevalence of fatigue, its associated factors, and the effect of tumor necrosis factor inhibitors (TNFi) on this subgroup of patients in a large axial spondyloarthritis (axSpA) cohort. METHODS: The study included 681 patients [ankylosing spondylitis (AS) and nonradiographic axSpA (nr-axSpA)]. The Fatigue Severity Scale (FSS) and the Bath AS Disease Activity Index question 1 (BASDAI Q1) indices were used for fatigue assessment. Severe fatigue was defined as an FSS ≥ 4 or a BASDAI Q1 ≥ 5. Disease activity, function, and quality of life (QoL) measures were recorded. Patients who had been treated with TNFi were identified, and baseline and followup data were analyzed. RESULTS: Of the cohort, 67.3% had severe fatigue, and the prevalence was similar between AS (67.2%) and nr-axSpA (68.2%). Severely fatigued patients tended to have higher disease activity scores, increased acute-phase proteins, and decreased QoL measures. TNFi therapy was associated with improvement in disease activity, and although this treatment led to significantly decreased fatigue scores, this reduction was not optimal in the majority of patients with 80% continuing to have severe fatigue according to their posttreatment scores. Health Assessment Questionnaire, mean scores of BASDAI Q5 and Q6, and BASDAI enthesitis were independent predictors of fatigue severity. CONCLUSION: Fatigue is a common symptom in axSpA, and the burden of fatigue among patients with nr-axSpA is similar to that seen in AS. While biologics are effective in improving disease activity, their effect on fatigue is more limited. In axSpA, fatigue remains unresponsive to TNFi in nearly 80% of patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".