Predictors of disability among Filipinos with knee osteoarthritis
Bibliographic record
Abstract
Abstract Aims: This study aims to describe the level of disability of Filipino patients with knee osteoarthritis (OA) in relation to common risk factors. Methodology: This is a cross‐sectional analytic study. Patients with knee osteoarthritis diagnosed using the American College of Rheumatology criteria for the classification of knee OA, seen at East Avenue Medical Center, using the Quezon City, Philippines, were entered by convenient sampling. The Western Ontario and McMaster Universities (WOMAC (va) 3.1 Tagalog Version) osteoarthritis index was used. Self‐reported disability was measured by the function subscale of the WOMAC OA index and used as the dependent variable. Independent variables assessed as possible risk factors affecting disability were age, sex, weight, height, body mass index (BMI), education (in years), number of comorbidities present, smoking status (pack years), duration of knee OA, pain and stiffness. Categories of disability were identified as high, moderate and low. Analyses of the data were performed using Statistical Package for the Social Sciences (SPSS) version 13. Results: Eighty‐five subjects were included in the study. The mean disability score was 674.1 ± 318.81 (moderate disability). Chi‐square tests showed that the categories or levels of disability are not significantly dependent on the categorical variables. Significant direct correlations were seen between mean disability and weight (r = 0.260, P = 0.016), pain (r = 0.574, P = 0.000), and stiffness (r = 0.616, P = 0.000). Conclusion: This is the first study analysing the relationship between disability and specific risk factors among Filipino patients with knee OA. Self‐reported disability of knee OA in the population studied was strongly related to pain scores, weight and joint stiffness scores.
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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 | 0.000 | 0.002 |
| 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) | 0.002 | 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".