Clinical Predictors of Functional Disability in Knee Osteoarthritis: Risk Stratification Approach as Implications of Nursing Practice
Bibliographic record
Abstract
Osteoarthritis (OA) of the knee is one of the most common musculoskeletal disorders and a leading cause of disability in the elderly. The severity of osteoarthritis can be assessed using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), which is a widely used tool to measure pain, stiffness, and physical dysfunction in patients with OA. This study aimed to analyze the relationship between the demographic and clinical characteristics of patients with knee osteoarthritis and their WOMAC scores. The study used a quantitative, cross-sectional observational design. The relationship between functional disability (measured by the WOMAC score) and various factors was analyzed. The independent variables included age, sex, occupation, body mass index (BMI), OA grade, and duration of OA to the WOMAC score. Significant correlations were found between the WOMAC score and both OA grade (p=0.049) and OA duration (p=0.030). Furthermore, the multiple linear regression analysis revealed that OA duration (p=0.038) and OA grade (p=0.036) were significant predictors of the WOMAC score, collectively explaining 13.0% of its variance (R² value = 0.130). The findings of this study indicate that OA grade and duration of illness are significant predictors of the level of disability as measured by the WOMAC scores. OA levels and disease duration were the primary predictors of functional disability, explaining 13.0% of the WOMAC score variance. Nursing interventions should focus on risk stratification based on these clinical markers, prioritizing early intervention for newly diagnosed patients, irrespective of their age or BMI.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".