Assessment of fatigue in patients with ankylosing spondylitis and analysis of its related factors
Bibliographic record
Abstract
Objective To investigate if the single item fatigue question of the Bath Ankylosing Spondylitis Disease Activity Index(BASDAI) is appropriate for measuring fatigue in patients with ankylosing spondylitis(AS), and to identify factors that influence fatigue in these patients. Methods A total of 93 patients with ankylosing spondylitis were recruited. Patients completed questionnaires on disease activity(BASDAI), functional ability[Bath Ankylosing Spondylitis Functional Index(BASFI)], the short form McGill Pain Questionnaire and 10 cm visual analogue scale(VAS) for sleep disturbance. Reliability was assessed with intraclass correlation coefficients. The patients were then dichotomized into a F+group (eg, fatigue is the major symptom) if the BASDAI fatigue scale was ≥5.0 and a F-group(eg, fatigue is the minor symptom) if the fatigue score was 5.0. Relationships between fatigue and clinical variables were examined using Pearson's correlation coefficient. Results There was good test retest reliability of BASDAI, BASFI and 10 cm VAS for sleep disturbance over 24 hours. Fifty three percent of patients ( n =49) were assigned to the F+group, they scored significantly worse when compared with those in the F-group with respect to the results with BASDAI, BASFI, the short form McGill Pain Questionnaire and 10 cm VAS for sleep disturbance( P 0.05). After controlling for other factors such as age, sex ratio, age at disease onset and disease duration, it was found that fatigue was highly correlated with pain, stiffness, disease activity, functional disability and sleep disturbance. Conclusion Fatigue is a major symptom in majority of the patients with AS, in particular those with more severe disease. Fatigue can effectively be measured with a single item fatigue question of BASDAI, and appears to be strongly associated with the level of pain, stiffness, disease activity, functional ability and sleep disturbance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".