Serum Cartilage Oligomeric Matrix Protein (Scomp) Is Elevated In Patients With Knee Osteoarthritis: A Systematic Review And Meta-Analysis
Bibliographic record
Abstract
Objective: To be used in diagnostic studies, it must be demonstrated that biomarkers can differentiate between diseased and non-diseased patients. Therefore, the purpose of this study was to answer the following questions: (1) Is serum cartilage oligomeric matrix protein (sCOMP) elevated in patients with radiographically diagnosed knee osteoarthritis (OA) compared to controls? (2) Are there differences in sCOMP levels when comparing differing radiographic OA severities to controls? Methods: Systematic review and meta-analysis. Data Sources: A systematic search of CINAHL, PEDro, Medline, and SportsDiscus was completed in March 2010. Keywords: knee, osteoarthritis, sCOMP, radiography. Study inclusion criteria: Studies were written in English, compared healthy adults with knee OA patients, used the Kellgren Lawrence (K/L) classi?cation, measured sCOMP, and reported means and standard deviations for sCOMP. Results: For question 1, seven studies were included resulting in seven comparisons. A moderate overall effect size (ES) indicated sCOMP was consistently elevated in those with radiographically diagnosed knee OA when compared to controls (ES ¼ 0.60, P < 0.001). For question 2, four studies were included resulting in 13 comparisons between radiographic OA severity levels and controls. Strong ESs were calculated for K/L-1 (ES ¼ 1.43, P ¼ 0.28), K/L-3 (ES ¼ 1.05, P ¼ 0.04), and K/L-4 (ES ¼ 1.40, P ¼ 0.003). A moderate ES was calculated for K/L-2 (ES ¼ 0.60, P ¼ 0.01). Conclusions: These results indicate sCOMP is elevated in patients with knee OA and is sensitive to OA disease progression. Future research studies with a higher level of evidence should be conducted to investigate the use of this biomarker as an indicator for OA development and progression.
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 | 0.016 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.029 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".