Longitudinal study of the bidirectional association between pain and depressive symptoms in patients with psoriatic arthritis
Bibliographic record
Abstract
OBJECTIVE: To test the bidirectional hypothesis that depressive symptoms influence changes in pain over time, and pain influences changes in depressive symptoms. METHODS: A total of 394 patients attending the University of Toronto Psoriatic Arthritis clinic were followed over a mean period of 7.5 years with annual assessments, including number of swollen joints (SJC), Health Assessment Questionnaire (HAQ), and the Medical Outcomes Survey Short Form 36 (SF-36). Linear mixed-effects models were used to examine the cross- and lagged associations between the changes in HAQ pain and in the SF-36 mental component summary (MCS) score, adjusting for SJC and other covariates. RESULTS: The strongest predictors of changes in pain, SJC, and depressive symptoms between visits were scores of the corresponding variables at the previous visit, with standardized regression coefficients exceeding 0.75 in absolute value. There was, however, evidence of a small, but consequential, bidirectional relationship (i.e., standardized regression coefficients <0.3) between depressive symptoms and pain. Both previous MCS scores and change in MCS scores were associated with change in pain between visits; conversely, previous pain scores and change in pain scores were associated with change in depressive symptoms between visits. CONCLUSION: Even though cross-variable associations between pain and depressive symptoms exist, changes in pain and depressive symptoms appear to be strongly driven by their measurements at the previous visit. To optimize patient outcomes, a clinical approach that assesses and treats clinically significant depressive symptoms, as well as pain, is required.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".