Correlation between pain severity and levels of anxiety and depression in osteoarthritis patients: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVES: Osteoarthritis (OA) is a chronic degenerative musculoskeletal disease that causes articular damage and chronic pain, with a prevalence of up to 50% in individuals >60 years of age. Patients suffering from chronic painful conditions, including OA, also frequently report anxiety or depression. A systematic review and meta-analysis were performed to assess the correlation between pain severity and depressive and anxious symptomatology in OA patients. METHODS: A systematic search was conducted using four databases (PubMed, Medline, Scopus, and Web of Science) from inception up to 14 January 2020. We included original articles evaluating pain severity and anxiety and/or depression severity in OA-diagnosed patients. Detailed data were extracted from each study, including patients' characteristics and pain, anxiety, and depression severity. When available, the Pearson correlation coefficient between pain and depression severity and pain and anxiety severity was collected, and a meta-analysis of random effects was applied. RESULTS: This systematic review included 121 studies, with a total of 38 085 participants. The mean age was 64.3 years old, and the subjects were predominantly female (63%). The most-used scale to evaluate pain severity was the Western Ontario and the McMaster Universities Osteoarthritis Index, while for anxiety and depression, the Hospital Anxiety and Depression Scale was the most used. The meta-analysis showed a moderate positive correlation between pain severity and both anxious (r = 0.31, P <0.001) and depressive symptomatology (r = 0.36, P <0.001). CONCLUSION: Our results demonstrate a significant correlation between pain and depression/anxiety severity in OA patients, highlighting the need for its routine evaluation by clinicians.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.037 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".