Canadian Inborn Errors of Immunity National Registry (CIEINR): A High-Quality Standardized Patient Data Platform to Support Patient Advocacy and Immune Deficiency Research
Bibliographic record
Abstract
Introduction Inborn errors of immunity (IEIs) comprise a heterogeneous group of rare disorders, characterized by a wide spectrum of immunological alterations that influence the presentation and age at onset of disease. Approximately 30,000 Canadians suffer from primary immunodeficiency. Canada is home to several specific populations with a higher incidence of unique IEIs. Canada lacks a comprehensive database detailing the epidemiology, clinical and immunological phenotypes, and genotypes of patients with IEIs. We developed the novel and innovative Canadian Inborn Errors of Immunity National Registry (CIEINR), a machine-readable, high-quality dataset that promotes research through standardized data exchange and supports patient advocacy. Methods CIEINR was established by a national steering committee of 13 clinician scientists from 9 Canadian provinces, through monthly virtual meetings. Following a literature review of existing international IEI registries, the peer-reviewed study protocol, consent forms, and governance documents were developed. ImmUnity Canada, the national patient organization, was consulted to review the protocol. Ontology-based data collection forms were developed in collaboration with bioinformatics scientists to capture input data in a structured fashion. Regulatory documents and standardized data collection forms were harmonized with United States Immunodeficiency Network and European Society for Immunodeficiencies to support data sharing, methodological consistency, and interoperability. A continuous quality improvement framework aligns with the Canadian Drug Agency’s Best Practices and Standards to Enhance the Quality of Rare Disease Registries in Canada. Results The CIEINR has been established and includes 25 centers across Canada. Electronic clinical research forms in the Research Electronic Data Capture (REDCap) platform were successfully piloted including the embedded analytic tools such as RareLink and Phenopackets on patients’ data with variable forms of IEIs. Conclusion By collecting high-quality, precise, ontology-based patient data, the CIEINR will improve understanding of the Canadian IEI landscape, identify challenges and opportunities for patients and their healthcare providers, and support research and advocacy.
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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 | 0.067 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.019 | 0.019 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".