Psychological Correlates of Self-reported Disease Activity in Ankylosing Spondylitis
Bibliographic record
Abstract
OBJECTIVE: To investigate the role of psychological variables in self-reported disease activity in patients with ankylosing spondylitis (AS), while controlling for demographic and medical variables. METHODS: Patients with AS (n = 294) meeting modified New York criteria completed psychological measures evaluating depression, resilience, active and passive coping, internality, and helplessness. Demographic, clinical, and radiologic data were also collected. Univariate and multivariate analyses were completed to determine the strength of the correlation of psychological variables with disease activity, as measured by the Bath AS Disease Activity Index (BASDAI). RESULTS: In the multivariate regression analysis, the psychological variables contributed significantly to the variance in BASDAI scores, adding an additional 33% to the overall R-square beyond that accounted for by demographic and medical variables (combined R-square 18%). Specifically, arthritis helplessness and depression accounted for the most significant portion of the variance in BASDAI scores in the final model. CONCLUSION: Arthritis helplessness and depression accounted for significant variability in self-reported disease activity beyond clinical and demographic variables in patients with AS. These findings have important clinical implications in the treatment and monitoring of disease activity in AS, and suggest potential avenues of intervention.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".