Relationships among walking speed, selected clinical symptoms, and exercise self-efficacy in individuals with knee osteoarthritis
Bibliographic record
Abstract
Purpose Osteoarthritis (OA) is characterised by a combination of joint symptoms and signs stemming from defects in the articular cartilage and adjacent tissues, such as bone, synovial joint capsule, muscles, and ligaments. Knee OA is among the most common causes of pain and disability in middle-aged and older people. This study investigated the relationships among selected clinical symptoms (pain, stiffness, physical function), walking speed, and exercise self-efficacy in individuals with knee OA in Ibadan, Nigeria. Methods The study involved 100 individuals diagnosed with knee OA. Exercise self-efficacy was assessed with the exercise self-efficacy scale. Pain, stiffness, and physical function were evaluated with the Western Ontario and McMaster (WOMAC) Osteoarthritis Index questionnaire. Walking speed was determined during the 20-meter walk test. Data were analysed with descriptive statistics and Pearson’s correlation test, with significance level set at 0.05. Results Significant correlations were observed between pain intensity and walking speed (ρ = –0.38), stiffness and walking speed (ρ = –0.19), physical function and walking speed (ρ = –0.40), pain intensity and exercise self-efficacy (ρ = –0.43), stiffness and exercise self-efficacy (ρ = –0.46), physical function and exercise self-efficacy (ρ = –0.41). More than 50% of the participants with knee OA had low exercise self-efficacy and moderate walking speed. Individuals with higher levels of pain, stiffness, and functional limitations showed lower levels of exercise self-efficacy and lower walking speed. Conclusions Individuals with knee OA presented low exercise self-efficacy, low walking speed, and reduced physical function, probably because of the debilitating effects of the condition.
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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 | 0.003 |
| 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 | 0.000 | 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".