Impact of Knee Pain and Osteoarthritis on Quality of Life: A Comprehensive Assessment of Physical, Social, and Psychological Factors
Bibliographic record
Abstract
Introduction: Osteoarthritis (OA) is a prevalent disease that affects the quality of life (QoL) not only through pain and physical disability but also by influencing social and psychological aspects of life. This study aims to compare patients diagnosed with knee OA to those with knee pain and other comorbidities to evaluate the specific impact of OA on QoL. Materials and Methods: A total of 150 patients presenting with knee pain or knee OA were assessed using standardized QoL instruments, including the World Health Organization QoL-BREF (WHOQOL-BREF), the Western Ontario and McMaster Universities Arthritis Index (WOMAC), the Brief Illness Perception Questionnaire (Brief IPQ), and the Patient Health Questionnaire-9 (PHQ-9). Results: The influence of various factors, such as patient characteristics, demographics, medical history, medication use, OA diagnosis, and related symptoms, was analyzed using regression models. Significant correlations were observed between QoL and variables including knee injuries (WOMAC score: 54.662 vs. 38.657, P < 0.001), depression (PHQ-9: 9.894 vs. 6.608, P < 0.001), elevated BMI (WOMAC: F = 5.305, P = 0.023), and occasional crepitus (WOMAC score: 53.144 vs. 40.175, P = 0.003). No statistically significant differences were found between patients with OA and those with other diagnoses in any of the outcome measures. Conclusions: The findings suggest that QoL is influenced more by general factors such as psychological well-being (depression), pain (knee injuries), and overall health (BMI) rather than the specific diagnosis of OA. This underscores the importance of addressing these broader health attributes to improve the QoL in patients with knee-related issues.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.000 | 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".