Use of Highly Sensitive C-Reactive Protein for Followup of Wegener’s Granulomatosis
Bibliographic record
Abstract
OBJECTIVE: Since Wegener's granulomatosis (WG) represents a relapsing disease, efforts have been made to reliably predict relapses using blood tests. Followup measures such as conventionally determined C-reactive protein (CRP), antineutrophil cytoplasmic antibody (C-ANCA) titer, and proteinase-3 (PR3) ELISA are applied. We evaluated whether during remission elevated highly sensitive CRP (hsCRP) precedes relapse as a marker of subclinical inflammation and thus might improve clinical assessment. METHODS: We investigated 227 sera of 57 patients with WG: 74 sera collected from patients in remission who subsequently relapsed (before relapse), 30 sera collected during relapse, and 123 sera from patients in remission without relapse. We also distinguished between major and minor relapse. hsCRP, conventionally determined CRP (CRP), C-ANCA, PR3-ELISA, and erythrocyte sedimentation rate (ESR) were measured using commercial kits, and levels were correlated to clinical status. RESULTS: Only hsCRP and ANCA titer, but not CRP levels, were higher in sera from patients who subsequently relapsed versus those who did not, indicating patients at risk. Levels of hsCRP, CRP, and ESR were higher in sera collected during relapse than in the sera before relapse. hsCRP, conventional CRP, and ESR were also higher in samples collected during major relapse than before major relapse. Looking at the levels just before relapse compared to previous levels during remission, none of these measures rose directly before the clinical manifestation of the relapse. CONCLUSION: Our study provides evidence for an additional value of hsCRP in the clinical assessment of patients with WG.
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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) | 0.000 | 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) | 0.000 | 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".