Serum Urate Levels Alter the Spatial Distribution of Urate Crystals in Synovium and Correlate With Synovitis and Pain in Non‐Gout Female Patients With Anteromedial Knee Osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: This study aimed to investigate alterations in the spatial distribution of urate crystals in the osteoarthritic synovium of patients with different levels of serum uric acid (SUA). Additionally, we examined the association between SUA levels and the severity of synovitis and pain in anteromedial osteoarthritis (AMOA) of the knee. METHODS: Patients who underwent knee arthroplasty due to AMOA were prospectively enrolled. Blood, synovial fluid, and synovium samples were collected and UA levels were quantified. The degree of synovitis was evaluated histologically, and the levels of inflammatory cytokines in the synovial fluid were measured. The spatial distribution of urate crystals in the synovium was determined using Gomori methenamine silver staining, and the pain subscale of the Western Ontario McMaster Universities Osteoarthritis Index was used to evaluate knee pain. The relationships among SUA, degree of synovitis, and knee pain were assessed using Spearman rank correlation and multivariable analyses. RESULTS: The pattern of urate crystal deposition in the osteoarthritic synovium was significantly altered in populations with different SUA levels. The capillary wall, sublining layer, and lining cells were sequentially affected. SUA level was the only risk factor for high-grade synovitis and severe pain in the multivariate analysis. SUA level was also positively correlated with UA level in the synovial fluid and synovium, Krenn histologic score of the synovium, knee pain, and inflammatory cytokine level in the synovial fluid. CONCLUSION: Spatial urate crystal distribution in the synovium was altered when SUA levels were elevated. Elevated SUA levels were also associated with aggravated synovitis and pain.
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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 | 0.000 | 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) | 0.001 | 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".