A descriptive analysis of autoimmune cytopenias in children with inborn errors of immunity
Bibliographic record
Abstract
Introduction: Inborn errors of immunity (IEI) are a heterogenous group of disorders that lead to impairment and dysfunction of one or more parts of the immune system. Autoimmunity and autoimmune cytopenias have increasingly been recognized as early markers of IEI. This study describes the type, severity and range of cytopenias in children with IEI of varying immunophenotypes at a single Canadian centre. Methods: A retrospective chart review was completed of children with IEI followed at the Stollery Children's Hospital in Edmonton, Alberta from January 2015 to December 2022. Patients were included if they received a diagnosis of an IEI and had single or multi-lineage cytopenia(s). The IEI diagnoses were grouped into immunophenotypic categories and cytopenias were compared using descriptive statistics. Results: Immune cytopenias were common in all phenotypic categories, though there was variability in the type and severity. Thrombocytopenia was most likely to be seen in combined (53.8%), syndromic (59%) and immune regulatory disorders (63.6%). Neutropenia was most common in phagocytic (71.4%), immune regulatory (63.7%) and humoral disorders (52.6%). Multi-lineage cytopenias were present in 67.2% of cases and 10.4% had persistent cytopenias. Conclusions: Immune cytopenias are common in varying types of IEI and are not isolated to a specific disease category. When seeing a patient with concerns for IEI, providers should investigate both for features of immunodeficiency and autoimmunity, in particular autoimmune cytopenias. The pattern of cytopenia may favour specific immunophenotypes and help prioritize further testing and timely referral.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".