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Record W4404924443 · doi:10.1542/peds.2024-069114nc

Measuring the Effect of Newborn Screening on Survival After Hematopoietic Cell Transplantation for Severe Combined Immunodeficiency: A 36-Year Longitudinal Study From the Primary Immune Deficiency Treatment Consortium

2024· article· en· W4404924443 on OpenAlexaboutno aff
Trang T. Le, Bird

Bibliographic record

VenuePEDIATRICS · 2024
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmunodeficiency and Autoimmune Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePrimary immunodeficiencyHematopoietic cellHematopoietic stem cell transplantationNewborn screeningSevere combined immunodeficiencyImmune systemTransplantationImmunodeficiencyImmunologyPediatricsHaematopoiesisInternal medicineStem cellGenetics

Abstract

fetched live from OpenAlex

To explore the effects of the implementation of population-based newborn screening for Severe Combined Immunodeficiency (SCID) on overall survival in patients with SCID after hematopoietic cell transplantation (HCT).After exclusions, the study included 902 children with SCID from 34 Primary Immune Deficiency Treatment Consortium sites in the United States and Canada who underwent allogeneic HCT between January 1, 1982 and December 31, 2018.Data such as sex, race and ethnicity, SCID type and genotype, trigger for diagnosis (newborn screening, family history, or clinical illness), age at HCT, time interval of HCT, infection status at HCT, transplant-related data, and date of death were collected. The Kaplan-Meier method was used to estimate overall survival. A risk adjustment model using Cox proportional hazards regression was used to analyze risk factors for HCT outcomes.Before 2010, the 5-year overall survival rate for children with SCID was 72% to 73%, with 32% to 33% of SCID diagnoses made because of preventative testing by family history, and 65% to 67% of diagnoses resulting from presenting clinical illness. Between 2010 and 2018, the 5-year overall survival rate significantly increased to 87%. Forty-nine percent of these diagnoses were made because of positive SCID newborn screening, whereas 33% of diagnoses resulted from presenting clinical illness. Since 2010, the 5-year overall survival rate was significantly higher in children diagnosed with SCID via newborn screening (92.5%) compared with children whose diagnosis resulted from presenting clinical illness (79.9%) or family history (85.4%). Risk factors for increased mortality, including active infection, age 3.5 months or older at HCT, Black or African American race, and specific SCID genotypes, were identified using multivariable analysis.There is marked benefit in population-based newborn SCID screening on overall survival of patients by facilitating early HCT and preventing infection.To our knowledge, this is the largest longitudinal multi-institutional study that has demonstrated the positive impact of population-based newborn SCID screening on the survival of patients with SCID who underwent HCT. This has led to a paradigm shift in the declining number of presenting infections and a higher frequency of HCT being performed before 3.5 months of age. Delayed SCID diagnosis can be a significant public health cost burden as these children will experience repeat hospitalizations, morbidity, and mortality. Newborn screening not only alleviates cost but also effectively treats these patients. Interestingly, African American children with SCID had the highest risk of death. Further studies are warranted to investigate factors contributing to disparities in care for this patient population.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.134

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.233
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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