C-reactive Protein Is a More Sensitive and Specific Marker for Diagnosing Bacterial Infections in Systemic Lupus Erythematosus Compared to S100A8/A9 and Procalcitonin
Bibliographic record
Abstract
OBJECTIVE: C-reactive protein (CRP), S100A8/A9, and procalcitonin have been suggested as markers of infection in patients with systemic lupus erythematosus (SLE). We investigated the clinical significance of these factors for indication of infection in SLE. METHODS: Blood samples were prospectively collected from 34 patients with SLE who had bacterial infections and 39 patients with SLE who had disease flares and no evidence of infection. A second set of serum samples was collected after the infections or flares were resolved. RESULTS: CRP levels of SLE patients with infections were higher than those with flares [5.9 mg/dl (IQR 2.42, 10.53) vs 0.06 mg/dl (IQR 0.03, 0.15), p < 0.001] and decreased after the infection was resolved. S100A8/A9 and procalcitonin levels of SLE patients with infection were also higher [4.69 μg/ml (IQR 2.25, 12.07) vs 1.07 (IQR 0.49, 3.05) (p < 0.001) and 0 ng/ml (IQR 0-0.38) vs 0 (0-0) (p < 0.001), respectively]; these levels were also reduced once the infection disappeared. In the receiver-operating characteristics analysis of CRP, S100A8/A9, and procalcitonin, the area under the curve was 0.966 (95% CI 0.925-1.007), 0.732 (95% CI 0.61-0.854), and 0.667 (95% CI 0.534-0.799), respectively. CRP indicated the presence of an infection with a sensitivity of 100% and a specificity of 90%, with a cutoff value of 1.35 mg/dl. CONCLUSION: Our data suggest that CRP is the most sensitive and specific marker for diagnosing bacterial infections in SLE.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".