Causes of hypereosinophilia in 100 consecutive patients
Bibliographic record
Abstract
BACKGROUND: /L) and hypereosinophilic syndrome (HES, HE with end-organ damage) are classified as primary (due to a myeloid clone), secondary (due to a wide variety of reactive causes), or idiopathic. Diagnostic evaluation of eosinophilia is challenging, in part because secondary causes of HE/HES such as lymphocyte-variant HES (L-HES) and vasculitis are difficult to diagnose, and emerging causes such as immunoglobulin G4-related disease (IgG4-RD) have rarely been examined. OBJECTIVE AND METHODS: We reviewed 100 consecutive patients with HE/HES who underwent extensive evaluation for primary and secondary eosinophilia at a single tertiary care center to determine causes of HE/HES in a modern context. RESULTS: Six patients had primary HE/HES, 80 had a discrete secondary cause identified, and 14 had idiopathic HE/HES. The most common causes of secondary eosinophilia were L-HES/HES of unknown significance (L-HESus) (20), IgG4-RD (9), and eosinophilic granulomatosis with polyangiitis (EGPA) (8). CONCLUSIONS: In contrast to other large published series of HE/HES, most patients in this study were found to have a discrete secondary cause of eosinophilia and only 14 were deemed idiopathic. These findings highlight the importance of extensive evaluation for secondary causes of eosinophilia such as L-HES, IgG4-RD, and EGPA.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".