Symptoms of Depression Predict the Trajectory of Pain Among Patients with Early Inflammatory Arthritis: A Path Analysis Approach to Assessing Change
Bibliographic record
Abstract
OBJECTIVE: To assess the longitudinal relationships, including directionality, among chronic pain, symptoms of depression, and disease activity in patients with early inflammatory arthritis (EIA). METHODS: One hundred eighty patients with EIA completed an examination, including swollen joint count, and were administered the Center for Epidemiological Studies Depression Scale (CES-D) and the McGill Pain Questionnaire (MPQ) at 2 timepoints 6 months apart. Cross-lagged panel path analysis was used to simultaneously assess concurrent and longitudinal relationships among pain, symptoms of depression, and number of swollen joints. RESULTS: Pain, symptoms of depression, and number of swollen joints decreased over time (p < 0.001) and were prospectively linked to pain, symptoms of depression, and number of swollen joints, respectively, at 6 months. Symptoms of depression and pain were correlated with each other at baseline (0.47) and at 6-month followup assessments (0.28). Baseline symptoms of depression significantly predicted pain symptoms at 6 months (standardized regression coefficient = 0.28, p = 0.001), whereas pain and disease activity did not predict the course of any other variable after controlling for baseline values. CONCLUSION: Symptoms of depression predicted the trajectory of pain from baseline to 6 months. In addition, there were reciprocal/bidirectional associations between pain and symptoms of depression over time. More research is needed to better understand the relationship between pain and depressive symptoms and how to best manage patients with EIA who have high levels of both.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".