A Unique Comprehensive Model to Screen Newborns for Severe Combined Immunodeficiency—An Ontario Single-Centre Experience Spanning 2013–2023
Bibliographic record
Abstract
BACKGROUND: Severe combined immunodeficiency (SCID) is a life-threatening genetic disorder caused by critical defects of the immune system. Almost all cases are lethal if not treated within the first two years of life. Early diagnosis and intervention are thus essential for improving patient outcomes. In 2013, Ontario became the first Canadian province to perform newborn screening (NBS) for SCID by T cell receptor excision circles (TRECs) analysis, a surrogate marker of thymic function and lymphocyte maturation. METHODS: This retrospective study reports on nearly 10 years of NBS for SCID at a quaternary referral centre. RESULTS: From August 2013 to April 2023, our centre's densely populated catchment area flagged 162 newborns with low TRECs levels, including 10 cases with SCID. Follow-up revealed other causes of low TRECs, including non-SCID T cell lymphopenia (secondary/reversible or idiopathic causes, and syndromic conditions) and prematurity. A small number of cases with normal repeat TRECs levels and/or T cell subsets were also flagged. Province-wide data from around this period revealed at least 24 diagnosed cases of SCID or Leaky SCID. CONCLUSIONS: This is the first report of NBS outcomes in a Canadian province describing the causative genetic defects, and the non-SCID causes of a positive NBS for SCID.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 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) | 0.003 | 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".