Correlation between Serum Level of Interleukin-6 and Osteoarthritis Disease Activity and Disability in Beni-Suef University Hospital
Bibliographic record
Abstract
Background: Osteoarthritis (OA) is considered one of the most common musculoskeletal disorders and the leading causes of disability nowadays. IL-6 plays a key role in local and systemic manifestation of OA. Objectives: The aims of this work were to evaluate the level of serum IL-6 and its correlation with the activity, severity, disability, early development of osteoarthritis and early structural bone damage in OA patients using Western Ontario and McMaster Universities Osteoarthritis (WOMAC), Modified Health Assessment Questionnaire disability (M.HAQ) and Kellgren and Lawrence (K-L) scores. Methodology: This case-control study had been conducted on 40 patients with OA attending the Rheumatology and Rehabilitation outpatient clinic, Beni-Suef University Hospital from December 2017 until April 2018. The study included 20 healthy individuals as well. The following parameters were investigated: IL-6 serum level, BMI, ESR, CRP, CBCs, Kidney function tests (blood urea and serum creatinine) and liver function tests (AST and ALT). Radiological assessment was done by plain X-ray to the affected joint. Drug history was taken stressing on steroid therapy. Data were processed and analyzed using computer-based program. Results: The results of the study revealed higher levels of IL-6 in serum of OA patients which was significantly correlated with WOMAC, M.HAQ, K-L scores, ESR and CRP. Conclusion: IL-6 may play a synergistic role in OA pathogenesis and the degree of severity and disability of the disease, so it can be used as a biomarker for disease diagnosis and predictor of disease progression.
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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 | 0.001 |
| 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 | 0.000 | 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".