Serum Level of Dickkopf-1 in Relation to Clinical, Radiological, and Ultrasonographic Findings in Patients with Primary Knee Osteoarthritis
Bibliographic record
Abstract
Background: A complex joint disease called osteoarthritis (OA) is characterized by cartilage breakdown and new bone production. A regulatory protein involved in bone formation and repair, Dickkopf-1 (Dkk-1) has garnered interest due to its possible connection to osteoarthritis (OA).Aim of the work: The purpose of the research Serum Dickkopf-1 Level and Clinical, Radiological, and Ultrasonographic Outcomes in Patients with Primary Osteoarthritis of the Knee.Patients and methods: This study included 40 participants with primary knee OA and 40 healthy controls. The collected data included demographic information, Western Ontario and McMaster University Arthritis Index (WOMAC), and Radiographic images used to determine the severity of OA using the Kellgren and Lawrence (K-L) grading system.Results: Dkk-1 levels were noticeably greater in OA patients than in healthy controls. Serum Dkk-1 levels and radiographic OA grades were shown to be significantly inversely correlated (P < 0.001). Individuals with knee effusion had considerably lower Dkk-1 levels than those without effusion. Moreover, a robust association was noted between Dkk-1 levels and the ultrasonography-measured thickness of the femoral cartilage.Conclusion: Dkk-1 is a promising biomarker for assessing the severity and progression of knee OA. Its elevated levels in OA patients, coupled with its correlation with radiographic and ultrasonographic findings, suggest that Dkk-1 may play a significant role in the degenerative processes of knee OA and could be useful in evaluating disease severity.
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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 | 0.000 | 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".