Associations between joint space narrowing and molecular markers of collagen and proteoglycan turnover in patients with knee osteoarthritis.
Bibliographic record
Abstract
OBJECTIVE: We examined whether plasma concentrations of biomarkers of the collagenase cleavage of type II collagen (C2C), types I and II collagens (C1,2C), type II collagen synthesis (CPII), proteoglycan aggrecan turnover (CS846), and the ratio C2C:CPII would distinguish subjects with progressive radiographic osteoarthritis (OA) from those with stable disease. METHODS: Subjects were 120 obese middle-aged women with unilateral knee OA who participated in a 30-month clinical trial of structure modification with doxycycline, in which a standardized semiflexed anteroposterior view of the knee was obtained at baseline, 16 months, and 30 months. Subjects were selected from a larger sample to permit a priori comparisons between 60 OA progressors and 60 nonprogressors, as defined by joint space narrowing (JSN) in the medial tibiofemoral compartment. Each group contained 30 subjects who exhibited clinically significant increases in knee pain over 30 months and 30 who did not. Plasma samples were obtained every 6 months for determination of C2C, CPII, CS846, and C1,2C. RESULTS: None of the biomarkers was a significant predictor of progression of JSN. Over the interval from baseline to 16 months, the mean and the maximum of the intercurrent CS846 values were significantly associated with JSN (i.e., 0.12-0.14 mm of JSN per SD decrease in mean or maximum CS846; p < 0.01). The mean of serial CS846 levels was related to JSN also during the interval between months 16 and 30. CONCLUSION: Markers of type II collagen synthesis/degradation and of proteoglycan aggrecan turnover were not predictive of JSN in knee OA in this pilot study. However, serial concentrations of proteoglycan aggrecan epitope CS846 were associated with JSN during both the intervals studied.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".