Evaluation of significance of clinical scoring systems in osteoarthritis knee in a South Indian population
Bibliographic record
Abstract
Introduction: Osteoarthritis is one of the most common joint disorders in adult population affecting about 10 % of global population above the age of 60 years. Knee osteoarthritis is an important contributor for global disability among the other osteoarthritis and it contributes for 83% of the global disease burden for osteoarthritis. The symptoms of knee osteoarthritis include pain, stiffness and activity induced swelling. Present study aims to validate the correlation between WOMAC index, a widely used clinical scoring system and radiologically proven osteoarthritis which is graded by Kellgren-Lawrence radiological grading system.Material and Methods: The present study was undertaken in a tertiary care teaching hospital by the Department of Orthopaedics, IGMC & RI, Puducherry. It was a hospital-based cross-sectional study. A sample of 240 respondents was selected based on inclusion criteria. Functional limitation and radiological assessment were done using Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and Kellgren and Lawrence (KL) scoring scale respectively. Observation and Results: The mean age of the study participants was 58.17±8.18. Chi square test was applied to test the correlation between WOMAC scoring and Kellgren-Lawrence radiological grading and found that there was significant correlation (P=0.0001 which was
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".