Interleukin 18: A Biomarker for Differential Diagnosis Between Adult-onset Still’s Disease and Sepsis
Bibliographic record
Abstract
OBJECTIVE: The differential diagnosis between rheumatic diseases and infectious conditions is a great challenge in clinical practice. Adult-onset Still's disease (AOSD) is a rare systemic inflammatory syndrome that shares several clinical and laboratory variables with sepsis. Interleukin (IL)-18 is overexpressed in AOSD, suggesting a possible role as a disease biomarker. The aim of our study was to detect IL-18 serum levels in a cohort of patients with AOSD and sepsis and to address its possible role as a biomarker for differential diagnosis. METHODS: A group of unselected patients with AOSD diagnosed according to the Yamaguchi criteria and consecutive patients with sepsis diagnosed according to the American College of Chest Physicians/Society of Critical Care Medicine Consensus Conference criteria were enrolled. The clinical and laboratory data were collected. In the AOSD group, disease activity was assessed by Pouchot's and Rau's criteria. IL-18 serum levels were detected by ELISA. RESULTS: Thirty-nine patients with AOSD and 18 patients with sepsis were enrolled. Two out of 18 patients with sepsis (11.1%) also fulfilled the Yamaguchi criteria. A significant difference was found in IL-18 serum levels between patients with active and inactive disease (p < 0.001), and it positively correlated with disease activity (p = 0.0003), ferritin serum level (p = 0.016), and erythrocyte sedimentation rate (p = 0.041). IL-18 was significantly increased in patients with AOSD when compared with sepsis (p = 0.014). For a cutoff of 148.9 pg/ml, this test had a specificity of 78.3% and a sensitivity of 88.6%. CONCLUSION: We have demonstrated that IL-18 can be a biomarker for differential diagnosis between AOSD and sepsis.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".