Impact of anxiety, depression, and stress among knee osteoarthritis patients: a percentage-based study
Bibliographic record
Abstract
Background: Mental health challenges play an important role in pain and progression of knee osteoarthritis (KOA). Two prevalent psychological comorbidities that affect patients' quality of life (QoL) are anxiety and depression. The functional condition of patients with KOA may be influenced by feelings of depression and anxiety. Methods: The study was conducted on 108 individuals diagnosed with osteoarthritis knee according to European League Against Rheumatism (EULAR) classification knee OA. Age, gender, and body mass index (BMI) were recorded, pain and functional activities were assessed using Western Ontario and McMaster Universities osteoarthritis index (WOMAC), and depression, anxiety and stress scales-21 (DASS-21) was used for stress, anxiety, and depression. Results: The 108 patients with osteoarthritis knee were included in the study. The mean age of patients was 55.92±0.8 years, and the mean BMI was 27.24±0.4 kg/m2. The majority of patients with knee OA had typical levels of stress (64.75%), anxiety (39.57%), and depression (50.36%). There was mild to severe degrees of stress (17.27–10.79%), anxiety (9.35–22.30%), and depression (20.14–22.30%). Anxiety had a higher prevalence of severe to extremely severe cases (28.78%) than depression (11.52%) and stress (7.19%), indicating the psychological load experienced by a subgroup of patients. Conclusions: According to the findings, to maximize patient care and rehabilitation, psychological support, especially for anxiety, should be incorporated into the treatment of 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".