Growth in diagnosis and treatment of primary immunodeficiency within the global Jeffrey Modell Centers Network
Bibliographic record
Abstract
BACKGROUND: Primary immunodeficiencies (PI), which include more than 450 single-gene inborn errors of immunity and may affect up to 1% of the population, are genetic disorders that impair the immune system. If not properly identified and treated, individuals with PI are subject to serious, prolonged, and sometimes life-threatening infections or autoimmunity. Despite advancements, awareness of PI remains a critical issue for physicians and the public alike, as this leads to the enhanced and expedited management of these conditions. To address this critical issue, the Jeffrey Modell Foundation (JMF) formed a global network of specialized centers. The goal of this endeavor was to raise awareness of PI to better identify, diagnose, and treat patients, reducing associated mortality and morbidity and improving quality of life (QOL). For more than two decades, the Jeffrey Modell Centers Network (JMCN) has served as the foundation upon which these goals have been pursued. The JMCN currently includes 909 Expert Physicians at 400 institutions, in 316 cities, and 86 countries spanning six continents. METHODS: A survey was developed by JMF for members of the JMCN, following the most recent Classification of PI from the IUIS Expert Committee, to periodically describe the patient population, including treatment modalities and demographics. Physician-reported data from 2021 was compared to that from 2018 and 2013. Physicians in the JMCN also reported on select outcomes of their PI patients one year prior to and one year following diagnosis. RESULTS: A total of 300 JMF Physician Surveys from 681 physicians were included in this analysis. This is a 75% physician response rate. From 2013 to 2021, there was a 96.3% increase in patients followed in the US and an 86.1% increase globally. During the same period, patients identified with a specific PI defect increased by 46.6% in the US and 47.9% globally. Patients receiving IgG and HSCT increased by 110% and 201% respectfully since 2013. Early diagnosis led to reported decreased morbidity and mortality and reduced calculated healthcare costs. CONCLUSIONS: This global analysis of physician-reported data on patients with PI demonstrates an increase in both diagnosed and treated patients. This substantial increase from within the JMCN is a testament to its impact. In addition to building an extensive global patient database, the expanding JMCN serves as a unique and critical resource, providing the infrastructure for earliest diagnosis, optimized treatments, and implementation of standard-of-care and best practices. The JMCN provides a critical platform that facilitates the education of physicians and patients, awareness initiatives, and research advances, through collaboration and connectivity, ultimately resulting in improved outcomes and QOL for patients with PI. The JMCN has steadily and substantially grown for more than two decades and continues to substantively impact the field of Immunology globally.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".