Usefulness of WOMAC index as a screening tool for knee osteoarthritis among patients attending a rural health care center in Tamil Nadu
Bibliographic record
Abstract
Background: Osteoarthritis is the eighth leading cause of disability and a degenerative disease that worsens over time. Hence, early diagnosis and treatment remains the key in the management of Osteoarthritis. The aim and objective of the study was to evaluate the usefulness of WOMAC index for screening Osteoarthritis among the patients older than 50 years of age attending a Rural Health Centre.Methods: A cross sectional study was done among patients complaining of knee pain who visited a rural centre between June-August 2016. Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) was applied to all participants with knee pain to assess risk for OA. Also, American College of Rheumatology criteria (ACR) was used as a standard. Descriptive statistics were used. Chi-square test with Odds Ratio was calculated to find out of the strength of association. To test the agreement of WOMAC score with ACR Clinical Criteria, McNemar’s test analysis was done.Results: Out of 103 study subjects 45 were males and 58 were females. According to WOMAC Index Scores, 20 (19.4%) subjects belonged to high risk (score ≥81) and 38 (39.6%) subjects belonged to moderate risk (score 60 - 80). The Mean±SD WOMAC Score was 64.40±15.2. Age group [OR 2.85; 95% CI 1.25-6.48) and gender [OR 2.29; 95% CI 1.01-5.24) were significantly associated with WOMAC score percentage. Comparing WOMAC score percentages with ACR criteria for knee OA revealed statistically significant agreement (p-value, 0.009) which indicated the diagnostic accuracy of WOMAC index.Conclusions: WOMAC Index can be a useful screening tool for people at risk for Osteoarthritis and will help in identifying the disease early.
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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 | 0.001 | 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".