Synovial Fluid Macrophage Migration Inhibitory Factor Levels Correlate with Severity of Self-Reported Pain in Knee Osteoarthritis Patients
Bibliographic record
Abstract
BACKGROUND Inflammation is considered as one of the main pathogeneses in OA-induced pain. Macrophage migration inhibitory factor (MIF) is a well known pro-inflammatory cytokine. We aimed to determine whether MIF levels in serum and synovial fluid (SF) are associated with severity of OA-induced pain. MATERIAL AND METHODS We recruited 226 patients with knee OA and 106 controls. Self-reported pain severity of OA patients was evaluated using the Western Ontario McMaster University Osteoarthritis (WOMAC) pain scores. MIF levels were detected using enzyme-linked immunosorbent assay (ELISA). RESULTS OA patients had similar serum MIF levels compared to controls (11.93 [5.68-18.10] vs. 10.06 [6.60-14.61] ng/ml, P>0.05). In OA patients, MIF levels in SF were dramatically lower compared to paired serum samples (3.39 [1.87-5.89] vs. 11.93 [5.68-18.10] ng/ml, P<0.01). MIF levels in SF were significantly correlated with WOMAC pain scores (r=0.237, P<0.001), but MIF levels in serum had no significant correlation with WOMAC pain scores (r=0.009, P=0.898). CONCLUSIONS MIF levels in SF, but not in serum, were independently associated with the severity of self-reported pain in OA patients. The inhibition of MIF signaling pathways may be a novel therapeutic approach for ameliorating OA-induced pain.
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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 | 0.000 |
| 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".