DIFFICULTIES IN THE EARLY DIAGNOSIS OF EOSINOPHILIC GRANULOMATOSIS WITH POLYANGIITIS (CHURGSTRAUSS SYNDROME) IN THE CLINIC OF INTERNAL DISEASES
Bibliographic record
Abstract
Systemic vasculitis are characterized by heterogeneity of clinical-immunological forms and determined the need for differential diagnostic search to except a wide range of diseases, such as allergic, infectious, hematological, oncological, which often presents significant difficulties for physicians of various specialties. The article presents clinical observations demonstrating the difficulties of diagnostic search in establishing the diagnosis of systemic vasculitis associated with antineutrophil cytoplasmic antibodies, which include a rare disease — eosinophilic granulomatosis with polyangiitis (Churg–Strauss syndrome). Carefully collected anamnesis, participation of specialists of different profiles, retrospective analysis of laboratory and instrumental data allowed to verify the diagnosis, to prescribe adequate therapy. The aim of the publication is to discuss the need for early diagnosis of eosinophilic granulomatosis with polyangiitis, which can improve the effectiveness of therapy and improve the overall prognosis for this disease, taking into account modern approaches based on the main provisions of international recommendations that were prepared in 2015 with the participation of leading experts from Europe, USA and Canada and were called to become the basis for choosing a personalized patient therapy strategy.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".