Anxiety and depression prevalence and associated factors in patients with knee osteoarthritis
Bibliographic record
Abstract
Objective With the evolving spectrum of diseases, psychological conditions such as anxiety and depression have emerged as significant global public health concerns. Notably, these psychological disorders are prevalent among patients suffering from knee osteoarthritis (KOA). Consequently, this study included 360 hospitalized patients diagnosed with KOA to examine their levels of anxiety and depression and to analyze the factors influencing these psychological states. Methods A cohort of KOA patients from the Second Hospital of Shanxi Medical University was assessed using a general condition questionnaire, European five-dimensional health status scale (EQ5D), Western Ontario and McMaster University Osteoarthritis (WOMAC), Social Support Rating Scale (SSRS), and Hospital Anxiety and Depression Scale (HADS). Logistic regression analysis was employed to identify the factors affecting anxiety and depression. Results Among 360 patients with KOA, 28.06% experienced anxiety, and 30.27% experienced depression. Multivariate logistic regression analysis showed that lower BMI, QOL, and utilization of social support scores are risk factors for anxiety and depression in KOA patients (P<0.05). Additionally, in patients with KOA, younger age, lower subjective support, and higher scores in function and daily activities emerged as significant risk factors for depression (P<0.05). Conclusion Anxiety and depression in patients with KOA warrant significant attention due to their impact on overall well-being. The factors influencing these mental health conditions are multidimensional. In clinical practice, it is essential to integrate these various influencing factors to develop targeted mental health care services. By doing so, healthcare providers can enhance the overall mental health and QOL for individuals suffering from KOA.
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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 | 0.000 | 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 | 0.000 | 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".