The interplay of body composition, pain intensity, and psychological status in Egyptian patients with knee osteoarthritis
Bibliographic record
Abstract
Abstract Background There is a clear relationship between poor mental health, obesity, and osteoarthritis (OA). Individuals who experience symptoms of anxiety and depression are more likely to report higher levels of pain and disability in OA. In addition, higher body mass index (BMI) levels can contribute to additional pain and disability in individuals with OA. We aimed to explore the interplay of Body composition, pain intensity, and psychological status in Egyptian patients with knee OA. Results This cross-sectional study included 60 patients with Knee OA diagnosed clinically according to the American College of Rheumatology (ACR) criteria. Body composition measurement was performed with an InBody analyzer. Pain was assessed by the pain visual analog scale (VAS), disability was measured with Western Ontario and McMaster Universities Osteoarthritis (WOMAC) scores, and depressed mood and/or anxiety was measured by the Hospital Anxiety and Depression Scale (HADS). Body mass index (BMI), total body fat (TBF) %, fat mass (FM), and fat mass index (FMI) were positively correlated with pain and disability in patients with knee OA. TBF% was positively correlated with depression and anxiety. Conclusions This study has shed light on the association between mental disorders, body composition measurements, knee pain, and disability. Interventions to treat osteoarthritis in elderly patients should focus on treating mood changes such as anxiety and depression, psychological support, and controlling body mass with proper diet and exercise programs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".