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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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 | 0.000 | 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".