Autoinflammatory gene mutations associated with eosinophilia and asthma
Bibliographic record
Abstract
BACKGROUND: Respiratory conditions, such as asthma, are infrequently associated with auto-inflammatory diseases. We describe five patients with uncontrolled respiratory symptoms that were seen at St. Joesph's Healthcare in Hamilton for severe asthma management diagnosed with rare autoinflammatory conditions using genetic molecular analysis. CASE PRESENTATION: Five patients are included in this case series. Gene mutations associated with familial Mediterranean fever, Yao syndrome, Cryopyrin-associated periodic syndrome, and Majeed syndrome were considered to explain partly the patient's clinical manifestation after comprehensive clinical, biochemical, hematological investigations ruled out other disorders such as parasitosis, Allergic Bronchopulmonary Fungosis, Eosinophilic Granulomatosis with Poly Angitis, IgG4 disease, and Hypereosinophilia syndrome. CONCLUSIONS: Complex patients initially presenting with respiratory conditions in addition to unexplained autoinflammatory conditions are a diagnostic challenge. Genetic molecular testing provides healthcare practitioners with useful information that may diagnose underlying auto-inflammatory diseases in undifferentiated patients. Role of inflammasome-activation in asthma and eosinophilia needs further investigation.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".