Factors influencing depression in community-dwelling elderly patients with osteoarthritis of the knee in China: a cross-sectional study
Bibliographic record
Abstract
Abstract Background Knee osteoarthritis (OA) and depression are both major health issues influencing the quality of elderly life. The aim of the present study was to explore the prevalence of depression and the factors influencing depression in community-dwelling elderly patients with OA of the knee in China. Methods We conducted a cross-sectional descriptive study. The study included 214 participants aged 60 and older diagnosed with OA of the knee. The depression of the elderly was measured by using the Geriatric Depression Scale (GDS). Participants were asked to complete a demographic questionnaire, the GDS, the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), the society dimension of Arthritis Impact Measurement Scales 2 (AIMS2). In addition, the participants performed a timed up and go test (TUG) and the stair-climb test (SCT). Results The average age of the participants was 69.2 ± 7.63 years old, their body mass index (BMI) was 25.2 ± 3.85, and their disease duration was 5.9 ± 7.72 years. The mean total score of the GDS was 4.43 ± 2.89, and the GDS scores correlated positively with pain (r = 0.45, P < 0.001), stiffness (r = 0.40, P < 0.001), physical function (r = 0.52, P < 0.001),TUG (r = 0.35, P < 0.001), and SCT (r = 0.47, P < 0.001) and negatively with social support (r = − 0.35, P < 0.001).Analysis using multiple regression demonstrated that physical function, social support, and SCT explained 36.8% of the variance in depression. Conclusions Our findings suggested that physical function, social support, and lower extremity strength were predictors of depressive symptoms in community-dwelling elderly people with OA of the knee. Focusing on this elderly group with increasing functional exercise, positive social interaction and support, and lower limb muscle strength training should help in the prevention of depression.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".