Patient‐reported Disease Activity in an Axial Spondyloarthritis Cohort during the COVID‐19 Pandemic
Bibliographic record
Abstract
OBJECTIVE: Response to the coronavirus disease 2019 (COVID-19) pandemic has resulted in shelter-in-place orders and major changes to individuals' daily lives. The impact of such stressors on disease activity in individuals with axial spondyloarthritis (axSpA) is unclear. The aim of this study is to examine whether stress, anxiety, and depression are associated with patient-reported disease activity, after accounting for important factors. METHODS: We administered a survey to an axSpA cohort from a single center with well-defined demographic and disease characteristics. We included questions about job status changes, exercise, medication use, disease activity (by the Bath Ankylosing Spondylitis Disease Activity Index [BASDAI]), and psychological factors (stress, depressive symptoms, and anxiety). Separate multivariable linear models examined the associations between perceived stress, anxiety, and depression with the BASDAI. RESULTS: After adjustment for potential confounders, those with higher levels of stress had a statistically significant 0.54-point higher BASDAI, on average, compared with those with lower levels of stress (95% confidence interval [CI]: 0.11, 0.97). Those with higher levels of anxiety also had a statistically significant higher BASDAI, on average, compared with those with lower levels of anxiety (β: 0.95, 95% CI: 0.18, 0.99). The association between depression and BASDAI was not statistically significant. We did not find differences in these associations among subgroups of age, job status, or county of residence. CONCLUSION: Individuals with axSpA with higher levels of stress and anxiety had significantly higher disease activity levels, although with a difference below clinical importance. Further planned studies will evaluate the trajectory of disease activity.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".