Intraarticular and Systemic Inflammatory Profiles May Identify Patients with Osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: To determine whether cytokine/chemokine profiles from synovial fluid and sera discriminate mild/moderate osteoarthritis (OA) from normal and severe OA cohorts. METHODS: Multiplex technology was used to quantify expression levels for 42 cytokines in the synovial fluid of patients diagnosed with severe OA (n = 20) and mild/moderate OA (n = 12), as well as normal controls (n = 34). The same 42 cytokines were examined in serum samples of patients with severe OA (n = 26) and mild/moderate OA (n = 74) and normal individuals (n = 100). Treatment group comparisons followed by principal component analysis (PCA) and K-means clustering of the significantly different cytokines/chemokines revealed groupings of patients by physician diagnosis. RESULTS: Differences in cytokine/chemokine levels were found between control, mild/moderate OA, and severe OA synovial fluid samples, as well as between normal and mild/moderate OA serum samples, and between control and severe OA serum samples. No differences were observed between mild/moderate and severe OA serum samples. Visual groupings based on PCA were validated by K-means analysis, with the best results obtained from the comparison of normal and mild/moderate OA serum samples with 96% of normal and 93% of mild/moderate OA samples accurately identified. CONCLUSION: Our study suggests that comparing the expression levels of cytokines/chemokines in synovial fluid and/or serum of patients with OA may have promise as a diagnostic platform to identify patients early in their disease course. This high-throughput low-cost assay may be able to provide clinicians with a diagnostic test to complement existing clinical and imaging modalities currently used to diagnose OA.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".