Relation of physical activity level with quality of life, sleep and depression in patients with knee osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: In the present study, we aimed to investigate the effects of physical activity level on the quality of life, depression, sleep quality and functional capacity in elderly patients with knee osteoarthritis (OA). METHODS: Fifty-five patients over 65 years of age (age range: 65-84 years) with knee osteoarthritis were enrolled in the study. Patients were divided into two groups including Insufficient Activity Group (IAG) and Physically Active Group (PAG) according to their responses to the International Physical Activity Questionnaire. Radiological OA grading was performed using Kellgren-Lawrence classification system. Patients were evaluated using Short-Form 36 (SF-36) questionnaire, Beck Depression Inventory (BDI), Pittsburgh Sleep Quality Index (PSQI) and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). RESULTS: Mean age, body mass indices, mean pain scores and gender distribution were comparable between the two groups. WOMAC physical function scores were lower in the Physically Active Group (p=0.01). Mean PSQI scores did not differ statistically significantly between the two groups (p=0.242). Mean BDI score of PAG was significantly lower compared to that of IAG (p=0.015). Mean SF-36 physical function (p=0.044), physical role (p=0.008) and physical component (p=0.016) scores of the Physically Active Group were significantly higher vs Insufficient Activity Group. CONCLUSION: Maintaining a high physical activity level reduces the possibility of depression and improves the quality of life and functional capacity in geriatric patients with knee osteoarthritis.
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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 | 0.000 | 0.000 |
| 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".