Value of Newborn Screening Programs for Severe Combined Immunodeficiency
Bibliographic record
Abstract
Severe Combined Immunodeficiency Disease (SCID) is life-threatening disease of infancy and childhood characterized by recurrent infections and failure to thrive. Given the modern medical progress made available for treating SCID, early identification of these children is paramount to their wellbeing and overall survival into adulthood. Newborn screening (NBS) programs provide the opportunity to identify SCID patients before life-threatening infections can manifest. The T-cell receptor excision circles (TRECs) assay currently used for SCID screening has been shown to satisfy all parameters of an effective screening test. Its widespread use is indicated by the time-sensitive nature of the disease, its efficacy in reducing morbidity and mortality in these patients, and the cost-effectiveness of prompt recognition versus long-term management. While immensely beneficial, screening tests still hold limitations that require analyzing. Follow-up measures for SCID identification programs have identified ambiguity and inconsistency among testing algorithms across facilities and technical errors that have causes inaccurate results. Considering fewer than 20% of SCID patients report a positive family history and the lethal consequences of disease if left untreated, a screening program is a highly valuable tool for early diagnosis and prompt intervention.
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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 | 0.003 | 0.025 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".