Assessment of Quality of Life in Patient with Knee Osteoarthritis
Bibliographic record
Abstract
Background: Knee osteoarthritis (OA) is a prevalent degenerative musculoskeletal disorder that significantly impairs the quality of life (QoL) of affected individuals, primarily impacting their physical capabilities and emotional well-being. Understanding the factors contributing to the quality of life in knee OA patients is crucial for developing effective management strategies. Objective: This study aims to ascertain the quality of life in patients suffering from knee osteoarthritis, emphasizing the role of demographic factors, physical limitations, and educational levels in influencing patient outcomes. Methods: A cross-sectional survey was conducted among 100 patients diagnosed with knee osteoarthritis, recruited from the Pakistan Society for the Rehabilitation of the Disabled (PSRD) over a four-month period. Participants were selected using a convenient sampling technique, with inclusion criteria set for individuals aged between 40 and 70 years who had a medical diagnosis of knee OA and were willing to sign the consent form. Data were collected through the AIMS-2 SF questionnaire and analyzed using SPSS version 25.0, focusing on demographic details, physical abilities and limitations, and overall quality of life scores. Results: The study population comprised 84% females and 16% males, with a significant portion having an intermediate level of education (35%). The quality of life scores indicated a poor to fair level of QoL among participants, with mean scores slightly below the borderline for being considered 'fair.' Physical limitations were significant, with 44% of participants experiencing difficulty in performing heavy physical activities daily and 36% facing challenges in walking or climbing stairs. Educational level emerged as a crucial factor, with lower educational attainment associated with poorer functional performance and quality of life. Conclusion: The findings highlight a predominantly poor to fair quality of life among knee OA patients, influenced significantly by gender, physical limitations, and educational levels. The study underscores the need for targeted interventions focusing on awareness, lifestyle modifications, and patient education to improve the quality of life in this population.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".