Molecular Testing in Sickle Cell Disease: From Newborn Screening to Transfusion Care
Bibliographic record
Abstract
Sickle cell disease (SCD) is one of the most frequent monogenic diseases worldwide and a highly heterogeneous and complex disease. SCD care carries several challenges. This includes early and accurate diagnosis as well as optimal red blood cell transfusion matching in this population carrying a high risk of alloimmunization. For decades, molecular biology has used hemoglobin and SCD as models for the development of several molecular tools. Such tools can now be used for various aspects of SCD care. Molecular diagnosis is notably the root of noninvasive prenatal testing. In postnatal diagnosis, including newborn screening, molecular approaches can overcome several limitations of protein-based methods. Simple approaches such as polymerase chain reaction can be used as a high-throughput and low-cost screening test. Moreover, combining sequence and deletion analyses allows for a comprehensive study of the β-globin locus, resolving complex cases. In transfusion care, genotyping for blood group determination has been shown to be more accurate compared to protein-based serological testing. Future development of molecular testing in SCD includes their use as prognostic tools and recent molecular diagnosis approaches. However, despite carrying major advantages, molecular testing may also present some limitations, such as high cost, limited accessibility in many countries, and limited information using targeted approaches. Molecular testing has a different pattern of advantages and limitations than protein-based analyses. Therefore, the optimal use of molecular testing is frequently not as a standalone approach but in combination with protein-based techniques. The optimal combination depends on the resources available and the clinical challenge, to ultimately improve SCD care.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".