Depression in patients with knee osteoarthritis: risk factors and associations with joint symptoms
Bibliographic record
Abstract
BACKGROUND: To describe demographic and clinical factors associated with the presence and incidence of depression and explore the temporal relationship between depression and joint symptoms in patients with symptomatic knee osteoarthritis (OA). METHODS: Three hundred ninety-seven participants were selected from a randomized controlled trial in people with symptomatic knee OA and vitamin D deficiency (age 63.3 ± 7.1 year, 48.6% female). Depression severity and knee joint symptoms were assessed using the patient health questionnaire (PHQ-9) and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), respectively, at baseline and 24 months. RESULTS: The presence and incidence of depression was 25.4 and 11.2%, respectively. At baseline, having younger age, a higher body mass index (BMI), greater scores of WOMAC pain (PR: 1.05, 95%CI:1.03, 1.07), dysfunction (PR: 1.02, 95%CI:1.01, 1.02) and stiffness (PR: 1.05, 95%CI: 1.02, 1.09), lower education level, having more than one comorbidity and having two or more painful body sites were significantly associated with a higher presence of depression. Over 24 months, being female, having a higher WOMAC pain (RR: 1.05, 95%CI: 1.02, 1.09) and dysfunction score (RR: 1.02, 95%CI: 1.01, 1.03) at baseline and having two or more painful sites were significantly associated with a higher incidence of depression. In contrast, baseline depression was not associated with changes in knee joint symptoms over 24 months. CONCLUSION: Knee OA risk factors and joint symptoms, along with co-existing multi-site pain are associated with the presence and development of depression. This suggests that managing common OA risk factors and joint symptoms may be important for prevention and treatment depression in patients with knee OA. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT01176344 . Anzctr.org.au identifier: ACTRN12610000495022 .
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".