Longitudinal Associations Between Depression, Anxiety, Pain, and Pain-Related Disability in Chronic Pain Patients
Bibliographic record
Abstract
OBJECTIVE: The current study sets out to examine the longitudinal relationship between pain, pain-related disability, and symptoms of depression and anxiety. The latter symptoms are highly prevalent in chronic pain and seriously impede functioning and quality of life. Nevertheless, the direction of the relationship involving these variables among individuals with chronic pain is still unclear. METHODS: Four-hundred twenty-eight individuals with chronic pain (238 women, mean age 54.84 years, mean pain duration 85.21 months) treated at two pain clinics completed questionnaires regarding their pain (Short-Form McGill Pain Questionnaire), depression (Center for Epidemiological Studies-Depression Scale), state anxiety (State-Trait Anxiety Inventory), and pain-related disability (Pain Disability Index) at four time points, with an average of 5 months between measurements. Cross-lagged, structural equation modeling analyses were performed, enabling the examination of longitudinal associations between the variables. RESULTS: Significant symptoms of both depression and anxiety were reported by more than half of the sample on all waves. A latent depression/anxiety variable longitudinally predicted pain (β = .27, p < .001) and pain-related disability (β = .38, p < .001). However, neither pain (β = .10, p = .126) nor pain-related disability (β = -.01, p = .790) predicted depression/anxiety. CONCLUSIONS: Among adult patients with chronic pain treated at specialty pain clinics, high levels of depression and anxiety may worsen pain and pain-related disability.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".