Serum and Synovial Fluid Levels of Interleukin-17A in Primary Knee Osteoarthritis Patients: Correlations With Functional Status, Pain, and Disease Severity
Bibliographic record
Abstract
Objectives: This study aims to assess the serum and synovial fluid (SF) levels of interleukin (IL)-17A in primary knee osteoarthritis (KOA) patients and to study their correlations with functional status, pain, and disease severity. Patients and methods: This cross-sectional study was conducted between December 2017 and March 2018 and it included 70 patients (46 males, 24 females; mean age 57.3±10.0 years; range 34 to 76 years) with primary KOA and 30 age-, sex-, and body mass index-matched healthy individuals (20 males, 10 females; mean age 53.3±10.3 years; range, 35 to 70 years). Western Ontario and McMaster Universities osteoarthritis index (WOMAC), visual analog scale (VAS), Lequesne index, and Kellgren and Lawrence (KL) grading scale were used for assessment of the disease. IL-17A levels were measured in the serum for patients and healthy controls, and in SF for patients only using an enzyme-linked immunosorbent assay. Results: Serum levels of IL-17A were significantly higher in KOA patients than controls (p=0.04). A positive correlation was found between serum and SF IL-17A levels. Serum and SF IL-17A levels had positive correlations with VAS, WOMAC pain score, Lequesne pain score, WOMAC function score, and Lequesne index. SF IL-17A levels had strong positive correlations with radiographic severity (KL grade) and duration of OA. Conclusion: Higher IL-17A levels in primary KOA patients were significantly associated with longer disease duration, higher pain scores, worse quality of life, extreme disability, and advanced structural damage. Therapeutics that target IL-17A warrant further investigation.
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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 | 0.002 |
| 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".