Analysing the association of BMI, physical activity and sociodemographics with osteoarthritis symptom severity: cross-sectional study in Southern Bosnia and Herzegovina
Bibliographic record
Abstract
OBJECTIVES: This study aimed to investigate the associations between body mass index (BMI), physical activity levels (PALs) and sociodemographic factors (gender, age, education and marital status) with different outcomes (symptoms) of osteoarthritis (OA) severity, in patients with knee OA. DESIGN: Cross-sectional study. PARTICIPANTS: The sample included 200 participants from southern Bosnia and Herzegovina (61 males, 65.1±9.01 years of age) who had been diagnosed with primary knee OA. OUTCOME MEASURES: OA symptoms as evidenced by the Western Ontario and McMaster Universities OA Index (WOMAC) scale, including three subscores (WOMAC-pain, WOMAC-stiffness, WOMAC-functionality) and total WOMAC score. The predictors included age (in years), gender (male or female), BMI, PAL, education level, urban/rural living environment and marital status (partnership). RESULTS: Female gender was correlated with the WOMAC-pain, WOMAC-stiffness and WOMAC-total. Older age was correlated with the WOMAC-pain and WOMAC-total. Patients who were better educated and reported higher PAL had better WOMAC functionality. BMI was the most significant factor of influence, with higher WOMAC-pain (OR 1.44, 95% CI 1.27 to 1.65), WOMAC-stiffness (OR 1.20, 95% CI 1.1 to -1.33), WOMAC-functionality (OR 1.26, 95% CI 1.13 to 1.40) and WOMAC-total (OR 1.29, 95% CI 1.6 to -1.44) scores in patients with higher BMI. CONCLUSIONS: Results indicate the necessity of controlling body weight in patients with diagnosed knee OA irrespective of gender. Further prospective studies are warranted in order to establish causality between variables.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".