Five Potentially Modifiable Factors Predict Poor Quality of Life in Ankylosing Spondylitis: Results from the Scotland Registry for Ankylosing Spondylitis
Bibliographic record
Abstract
OBJECTIVE: A chronic inflammatory condition manifesting in young adulthood, ankylosing spondylitis (AS) affects both physical and emotional quality of life (QOL). To inform future intervention strategies, this study aimed to (1) assess the QOL of patients with AS, and (2) identify potentially modifiable factors associated with reporting poor QOL. METHODS: The Scotland Registry for Ankylosing Spondylitis collects clinical and patient-reported data on clinically diagnosed patients with AS across Scotland. QOL is measured using the ASQoL questionnaire [range: 0 (high) to 18 (poor)]. Potentially modifiable factors associated with reporting poor QOL (score 12-18) were examined using Poisson regression models, adjusted for a variety of demographic characteristics, plus various nonmodifiable factors. Results are given as risk ratios (RR) with 95% CI. RESULTS: Data were available on 959 patients: 74% male, mean age 52 years (SD 13), median ASQoL 7.0 (interquartile range 2-12). Although many factors were univariately associated with poor QOL, 5 were identified as independent predictors: reporting moderate/severe fatigue (RR 1.60, 95% CI 1.13-2.28), poor physical function [Bath Ankylosing Spondylitis Functional Index (BASFI) ≥ 4: 3.46, 1.76-6.82], chronic widespread pain (CWP; 1.92, 1.33-2.75), high disease activity [Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) ≥ 4: 1.52, 1.09-2.12], and poor spinal mobility [Bath Ankylosing Spondylitis Metrology Index (BASMI) ≥ 4: 1.52, 0.93-2.50]. For these factors, population-attributable risks ranged between 20% (disease activity) and 56% (physical function). CONCLUSION: We have identified 5 potentially modifiable factors independently associated with poor QOL. These findings provide evidence that in addition to traditional clinical targets (BASDAI, BASFI, and BASMI), focus on nonspecific symptoms (CWP and fatigue), perhaps with nonpharmacological therapies, may yield important improvements in QOL.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".