Role of Hyaluronic Acid in Early Diagnosis of Knee Osteoarthritis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: Osteoarthritis of knee is traditionally diagnosed on the basis of clinical and radiological findings. Usually joint tissue degeneration is already advanced by the time a clinical diagnosis is made, hence the research focus has now shifted to use of biomarkers to diagnose the condition at an early stage of the disease. AIMS & OBJECTIVES: The aim of this study was to assess the efficacy of serum HA levels in early detection and grading of the severity of primary knee osteoarthritis and it's co-relation with Western Ontario and McMaster university osteoarthritis index (WOMAC scores) and Kellgren -Lawrence grading (K-L grade). MATERIALS AND METHODS: The study included 150 subjects (100 cases and 50 controls) and all were subjected to WOMAC scoring and K-L grading and estimation of serum HA levels. RESULTS: Age and WOMAC scores have significant correlation with HA levels, but multivariate analysis shows only WOMAC score as an independent variable associated with HA levels. The results show statistically significant high HA levels in cases than in normal population. HA levels are also able to differentiate between various clinical severity grades. ROC Curve analysis suggests cut-off levels of HA between mild, moderate and severe cases. CONCLUSION: HA levels are able to differentiate between normal asymptomatic population and symptomatic cases and also between various severity grades of osteoarthritis.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it