Relationships between Serum Interleukin-6, Radiographic Severity and WOMAC Index in Patients with Primary Knee Osteoarthritis
Bibliographic record
Abstract
Background: Osteoarthritis is the most common degenerative joint disease resulting in pain and altered joint function. Objective: We investigated the possible association between serum interleukin-6 and symptoms of knee osteoarthritis with regard to pain, stiffness, physical function, assessed by the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). We also examined the connections between serum interleukin-6 and radiographic severity in primary knee osteoarthritis patients. Methods: In this case-control study, fifty primary knee osteoarthritis patients and fifty age and sex matched controls were randomly recruited. Serum interleukin-6 levels were immunoassayed in patients’ and controls’ serum. Patients’ knee pain, stiffness and physical function were assessed by the respective subscales of the WOMAC Index. Standing anteroposterior radiographs of the knee joint were performed and graded with the Kellegren-Lawrence grade. Results: The mean serum IL-6 level was significantly higher in osteoarthritis patients (110.22 ± 46.98pg/ml) than controls (46.04 ± 12.34 pg/ml) (p=0.001). The WOMAC Index in patients ranged from 0 – 95 and the Kellegren-Lawrence score mean was 2.7 ± 0.76. There was a significant correlation between serum IL-6 levels and pain (r=0.595 p=0.001), physical function score (r=0.666, p=0.001)), and the radiographic score (r=0.799, p=0.001). Regression analysis showed that IL-6 level had a greater impact on both the WOMAC Index (p=0.005) and the Kellegren-Lawrence score (p=0.01). Conclusion: Serum IL-6 level is increased in primary knee osteoarthritis patients. Also, serum interlukin-6 is significantly related to osteoarthritis symptoms and radiographic severity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".