The association of plasma IL-1Ra and related cytokines with radiographic severity of early knee osteoarthritis
Bibliographic record
Abstract
We aimed to evaluate the association between inflammatory biomarkers in peripheral blood and severity of knee osteoarthritis (OA). We performed a cross-sectional study in participants with frequent knee pain, evaluated radiographic and clinical severity. We measured inflammatory biomarkers: plasma (p) IL-1Ra, IL-1β, IL-18, serum (s) CD14, hsCRP and bone and cartilage biomarkers: urine (u) CTX-II, (s) HA, COMP, CTX-I, PIIANP. We assessed radiographic severity by Kellgren-Lawrence (KL) grading and Osteoarthritis Research Society International (OARSI) standardized scoring atlas; and clinical severity by the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). 139 participants (82% women, mean ± SD age: 55.5 ± 7.8 years) were included. (p) IL-1Ra was negatively associated with radiographic severity by KL grading (Spearman rho = −0.197, P = 0.021), osteophytes (Spearman rho = −0.217, P = 0.011), and joint space narrowing of index knee (Spearman rho = −0.172, P = 0.045); and KL sum score of both knees (Spearman rho = −0.180, P = 0.035), after adjustment for age, gender and body mass index (BMI). Other inflammatory markers were not associated with radiographic severity. Cartilage degradation markers (u) CTXII and (s) COMP were modestly associated with radiographic severity after adjustment. In multivariate models, (s) hsCRP and the bone and cartilage biomarkers, but not the inflammatory biomarkers, were associated with radiographic severity. Among the inflammatory biomarkers in peripheral blood, IL-1Ra was negatively associated with radiographic severity in this early knee OA cohort.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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".