Factors affecting pain and physical functions in patients with knee osteoarthritis: An observational study
Bibliographic record
Abstract
Knee osteoarthritis (KOA) is more common as people age and have a higher body mass index (BMI). We must know the role of various factors in pain and physical functions in patients with KOA. Therefore, the present study sought to examine the factors associated with pain and physical functions in individuals with KOA. This cross-sectional observational study included patients with KOA (n = 125; 57 men, 68 women; mean age 52.9 years). Using the visual analogue scale and a reduced version of the Western Ontario McMaster Universities Osteoarthritis Index, pain severity and physical functions were assessed. Demographic factors such as age, BMI, sex, and Kellgren-Lawrence (K/L) radiographic grade of KOA were analyzed. Age (R = 0.263, P < .001), BMI (R = 0.379, P < .001), and K/L grade (R = 0.844, P < .001) were significantly associated with knee pain. Similarly, age (R = 0.310, P < .001), BMI (R = 0.374, P < .001), and K/L grade (R = 0.862, P < .001) were associated with physical functions. No significant association of sex with pain (R = 0.071, P = .440) and physical functions (R = 0.055, P = .545) was observed. Age, BMI, and K/L grade explained 71% and 74% of knee pain and physical functions, respectively. Age, BMI, and radiographic (K/L) grades were associated with pain and physical functions in patients with KOA. K/L grade was the most significant predictor of pain and physical functions in 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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".