The Alberta Newborn Screening Approach for Sickle Cell Disease: The Advantages of Molecular Testing
Bibliographic record
Abstract
Sickle cell disease (SCD), a group of inherited red blood cell (RBC) disorders caused by pathogenic variants in the beta-globin gene (HBB), can cause lifelong disabilities and/or early mortality. If diagnosed early, preventative measures significantly reduce adverse outcomes related to SCD. In Alberta, Canada, SCD was added to the newborn screening (NBS) panel in April 2019. The primary conditions screened for are sickle cell anemia (HbS/S), HbS/C disease, and HbS/β thalassemia. In this study, we retrospectively analyzed the first 19 months of SCD screening performance, as well as described our approach for screening of infants that have received a red blood cell transfusion prior to collection of NBS specimen. Hemoglobins eluted from dried blood spots were analyzed using the Bio-Rad™ VARIANT nbs analyzer (Bio-Rad Laboratories, Inc., Hercules, CA, USA). Targeted sequencing of HBB was performed concurrently in samples from all transfused infants. During the period of this study, 43 of 80,314 screened infants received a positive NBS result for SCD, and of these, 34 were confirmed by diagnostic testing, suggesting a local SCD incidence of 1:2400 births. There were 608 infants with sickle cell trait, resulting in a carrier frequency of 1:130. Over 98% of non-transfused infants received their NBS results within 10 days of age. Most of the 188 transfused infants and 2 infants who received intrauterine transfusions received their final SCD screen results within 21 ± 10 d of birth. Our SCD screening algorithm enables detection of affected newborns on the initial NBS specimen, independent of the reported blood transfusion status.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".