The role of sclerostin in knee osteoarthritis and its relation to disease progression
Bibliographic record
Abstract
Abstract Background Osteoarthritis (OA) is a common joint disease especially in aging population and is characterized by progressive degeneration of articular cartilage, osteophyte formation, and subsequent joint space narrowing. Sclerostin, a protein product of the SOST gene, secreted mainly by osteocytes causes inhibition of Wnt/ β-catenin signaling pathway and bone morphogenetic protein, therefore may affect bone formation and bone remodeling in OA. Aim The aim was to assess serum sclerostin level in patients with knee osteoarthritis (KOA) and its relation to disease severity. Patients and methods A total of 80 participants (50 KOA patients and 30 healthy controls) were recruited in the present study. Sclerostin level in plasma was assessed using an enzyme-linked immunosorbent assay. OA grading was performed using the Kellgren–Lawrence classification. Assessment of physical disability was done by Western Ontario and McMaster universities Arthritis index score and health assessment questionnaire score. Results Plasma sclerostin levels were significantly lower in patients with OA than in healthy controls (P<0.001). Moreover, serum sclerostin level demonstrated a significant inverse correlation with the physical disability score (r=−0.506, P<0.01), age (r=−0.295, P<0.01), disease duration (P<0.05), and radiographic severity of KOA (P<0.001). By univariate regression analysis, sclerostin was one of the strong negative predictors for severity of OA. Conclusion Sclerostin was significantly lower in OA plasma samples when compared with healthy controls. Serum sclerostin level was inversely associated with the physical disability and radiographic severity of KOA. Therefore, sclerostin may be used as a biochemical marker for reflecting disease severity in primary KOA.
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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) | 0.001 | 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".