Long term longitudinal follow-up of an AD-HIES cohort: the impact of early diagnosis and enrollment to IPINet centers on the natural history of Job’s syndrome
Bibliographic record
Abstract
Job's syndrome, or autosomal dominant hyperimmunoglobulin E syndrome (AD-HIES, STAT3-Dominant Negative), is a rare inborn error of immunity (IEI) with multi-organ involvement and long-life post-infective damage. Longitudinal registries are of primary importance in improving our knowledge of the natural history and management of these rare disorders. This study aimed to describe the natural history of 30 Italian patients with AD-HIES recorded in the Italian network for primary immunodeficiency (IPINet) registry. This study shows the incidence of manifestations present at the time of diagnosis versus those that arose during follow up at a referral center for IEI. The mean time of diagnostic delay was 13.7 years, while the age of disease onset was < 12 months in 66.7% of patients. Respiratory complications, namely bronchiectasis and pneumatoceles, were present at diagnosis in 46.7% and 43.3% of patients, respectively. Antimicrobial prophylaxis resulted in a decrease in the incidence of pneumonia from 76.7% to 46.7%. At the time of diagnosis, skin involvement was present in 93.3% of the patients, including eczema (80.8%) and abscesses (66.7%). At the time of follow-up, under therapy, the prevalence of complications decreased: eczema and skin abscesses reduced to 63.3% and 56.7%, respectively. Antifungal prophylaxis decreased the incidence of mucocutaneous candidiasis from 70% to 56.7%. During the SARS-CoV-2 pandemic, seven patients developed COVID-19. Survival analyses showed that 27 out of 30 patients survived, while three patients died at ages of 28, 39, and 46 years as a consequence of lung bleeding, lymphoma, and sepsis, respectively. Analysis of a cumulative follow-up period of 278.7 patient-years showed that early diagnosis, adequate management at expertise centers for IEI, prophylactic antibiotics, and antifungal therapy improve outcomes and can positively influence the life expectancy of patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".