The diagnosis of severe combined immunodeficiency: Implementation of the PIDTC 2022 Definitions
Bibliographic record
Abstract
Background Shearer et al in 2014 articulated well-defined criteria for the diagnosis and classification of severe combined immunodeficiency (SCID) as part of the Primary Immune Deficiency Treatment Consortium's (PIDTC's) prospective and retrospective studies of SCID. Objective Because of the advent of newborn screening for SCID and expanded availability of genetic sequencing, revision of the PIDTC 2014 Criteria was needed. Methods We developed and tested updated PIDTC 2022 SCID Definitions by analyzing 379 patients proposed for prospective enrollment into Protocol 6901, focusing on the ability to distinguish patients with various SCID subtypes. Results According to PIDTC 2022 Definitions, 18 of 353 patients eligible per 2014 Criteria were considered not to have SCID, whereas 11 of 26 patients ineligible per 2014 Criteria were determined to have SCID. Of note, very low numbers of autologous T cells (<0.05 × 10 9 /L) characterized typical SCID under the 2022 Definitions. Pathogenic variant(s) in SCID-associated genes was identified in 93% of patients, with 7 genes ( IL2RG , RAG1 , ADA , IL7R , DCLRE1C , JAK3 , and RAG2 ) accounting for 89% of typical SCID. Three genotypes ( RAG1 , ADA , and RMRP ) accounted for 57% of cases of leaky/atypical SCID; there were 13 other rare genotypes. Patients with leaky/atypical SCID were more likely to be diagnosed at more than age 1 year than those with typical SCID lacking maternal T cells: 20% versus 1% ( P < .001). Although repeat testing proved important, an initial CD3 T-cell count of less than 0.05 × 10 9 /L differentiated cases of typical SCID lacking maternal cells from leaky/atypical SCID: 97% versus 7% ( P < .001). Conclusions The PIDTC 2022 Definitions describe SCID and its subtypes more precisely than before, facilitating analyses of SCID characteristics and outcomes.
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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 | 0.027 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".