Follow-Up for an Abnormal Newborn Screen for Severe Combined Immunodeficiencies (NBS SCID): A Clinical Immunology Society (CIS) Survey of Current Practices
Bibliographic record
Abstract
Severe combined immunodeficiency (SCID) includes a group of monogenic disorders presenting with severe T cell lymphopenia (TCL) and high mortality, if untreated. The newborn screen (NBS) for SCID, included in the recommended universal screening panel (RUSP), has been widely adopted across the US and in many other countries. However, there is a lack of consensus regarding follow-up testing to confirm an abnormal result. The Clinical Immunology Society (CIS) membership was surveyed for confirmatory testing practices for an abnormal NBS SCID result, which included consideration of gestational age and birth weight, as well as flow cytometry panels. Considerable variability was observed in follow-up practices for an abnormal NBS SCID with 49% confirming by flow cytometry, 39% repeating TREC analysis, and the remainder either taking prematurity into consideration for subsequent testing or proceeding directly to genetic analysis. More than 50% of respondents did not take prematurity into consideration when determining follow-up. Confirmation of abnormal NBS SCID in premature infants continues to be challenging and is handled variably across centers, with some choosing to repeat NBS SCID testing until normal or until the infant reaches an adjusted gestational age of 37 weeks. A substantial proportion of respondents included naïve and memory T cell analysis with T, B, and NK lymphocyte subset quantitation in the initial confirmatory panel. These results have the potential to influence the diagnosis and management of an infant with TCL as illustrated by the clinical cases presented herein. Our data indicate that there is clearly a strong need for harmonization of follow-up testing for an abnormal NBS SCID result.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".