Cytokine Profiles of Macrophage Activation Syndrome Associated with Rheumatic Diseases
Bibliographic record
Abstract
OBJECTIVE: To elucidate the cytokine profiles of macrophage activation syndrome (MAS) in relation to underlying rheumatic diseases and prognosis. METHODS: The clinical features and laboratory data of 18 patients with MAS and rheumatic diseases were retrospectively analyzed. Serum levels of macrophage colony-stimulating factor (M-CSF), interleukin 18 (IL-18), tumor necrosis factor-alpha, interleukin 6, interferon-gamma, ferritin, and beta2-microglobulin (beta2m) were measured. These data were compared between underlying diseases and between those who died and those who recovered. RESULTS: Of the 18 patients with MAS, 9 had underlying systemic lupus erythematosus (SLE), 7 had adult-onset Still's disease (AOSD), 1 had rheumatoid arthritis (RA), and 1 had antiphospholipid syndrome. Three patients with SLE and 1 patient with RA died. The serum M-CSF and IL-18 levels were substantially elevated in all the patients. In the patients with SLE, the M-CSF level was higher than the IL-18 level (median: 4879 vs 1341 pg/ml, p = 0.0054), and it was the reverse in the patients with AOSD (5883 vs 228,350 pg/ml, p = 0.0017). The serum M-CSF and beta2m levels were significantly higher in the patients who died than in those who recovered (M-CSF: 18,245 vs 3404 pg/ml, p = 0.019; beta2m: 18.8 vs 5.4 mg/dl, p = 0.0058). CONCLUSION: The cytokine profiles associated with MAS differed between patients with SLE and patients with AOSD. The patients with SLE showed a prominent increase in serum M-CSF levels, as did the patients with AOSD in serum IL-18 level. Patients who died had higher serum M-CSF and ss(2)m levels, and this suggests that aggressive treatment for patients with MAS and these profiles should be promptly started.
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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) | 0.000 | 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".