Association of UCMA levels in serum and synovial fluid with severity of knee osteoarthritis
Bibliographic record
Abstract
AIM: Osteoarthritis (OA) is one of the most common joint diseases causing physical disability in the aged population. OA pathogenesis is not fully known and yet there are no effective therapeutic options against OA. Upper Zone of Growth Plate and Cartilage Matrix Associated (UCMA) is a member of vitamin K-dependent protein family, and is involved in inflammation, cardiovascular diseases, cancer, and OA. In the present study, our aim was to detect serum and synovial fluid (SF) levels of UCMA and to analyze their correlation with radiographic findings and symptomatic severity in OA patients as well as the correlation between oxidative stress levels and SF UCMA levels. METHODS: Forty OA patients with cartilage degeneration and 20 patients with other knee joint disorders (non-OA control) were included in the present study. We used the Kellgren-Lawrence (KL) classification and Western Ontario McMaster University Osteoarthritis Index (WOMAC) scores to assess radiographic grading and symptomatic severity of OA, respectively. UCMA levels were measured in SF and serum. And also oxidative stress markers were analyzed in SF. RESULTS: SF UCMA levels of OA patients were higher compared to those of the non-OA control group and were positively correlated with radiographic finding and symptomatic severity of OA. However, there was no significant correlation between oxidative markers of SF and the KL grade, WOMAC scores, and SF UCMA levels in OA patients. CONCLUSION: There is a close connection between UCMA SF levels and symptomatic and radiographic severities of knee OA. Therefore, UCMA can be a promising biomarker in the diagnosis and/or prognosis of OA disease.
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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 | 0.000 | 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".