Retrospective TREC testing of newborns with Severe Combined Immunodeficiency and other primary immunodeficiency diseases
Bibliographic record
Abstract
In Manitoba, Canada, the overall incidence of Severe Combined Immunodeficiency (SCID) is three-fold higher than the national average, with SCID overrepresented in two population groups: Mennonites and First Nations of Northern Cree ancestries. T-cell receptor excision circle (TREC) assay is being used increasingly for neonatal screening for SCID in North America. However, the majority of SCID patients in Manitoba are T-cell-positive. Therefore it is likely that the TREC assay will not identify these infants. The goal of this study was to blindly and retrospectively perform TREC analysis in confirmed SCID patients using archived Guthrie cards. Thirteen SCID patients were tested: 5 T-negative SCID (3 with adenosine deaminase deficiency, 1 with CD3δ deficiency, and 1 unclassified) and 8 T-positive SCID (5 with zeta chain-associated protein kinase (ZAP70) deficiency and 3 with inhibitor of kappa light polypeptide gene enhancer in B-cells, kinase beta (IKKβ) deficiency). As a non-SCID patient group, 5 Primary Immunodeficiency Disease (PID) patients were studied: 1 T-negative PID (cartilage-hair hypoplasia) and 4 T-positive PID (2 common immune deficiency (CID), 1 Wiskott–Aldrich syndrome, and 1 X-linked lymphoproliferative disease). Both patient groups required hematopoietic stem cell transplantation. In addition, randomly-selected de-identified controls (n = 982) were tested. Results: all T-negative SCID and PID had zero TRECs. Low-TRECs were identified in 2 ZAP70 siblings, 1 CID patient as well as 5 preterm, 1 twin, and 4 de-identified controls. Conclusions: TREC method will identify T-negative SCID and T-negative PID. To identify other SCID babies, newborn screening in Manitoba must include supplemental targeted screening for ethnic-specific mutations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".