Reconsidering sickle cell trait testing of red blood cell units allocated to children with sickle cell disease
Bibliographic record
Abstract
BACKGROUND: Sickle cell trait (SCT) testing of red blood cell (RBC) units is sometimes performed to identify and divert units containing hemoglobin S (HbS). Recipients strategically guarded against this exposure include fetuses, neonates, and children with sickle cell disease (SCD). The clinical necessity of this practice is unclear. STUDY DESIGN AND METHODS: A one-year audit (2018) was performed at a pediatric tertiary care hospital that tests for SCT in RBC units prescribed to children with SCD and neonates. The impact of incorporating varying numbers of SCT RBC units in a single-unit top-up, partial-manual red cell exchange, and automated erythrocytapheresis was modeled in four typical-parameter age scenarios (2, 5, 10, and 18 years) sharing a high baseline HbS. Additionally, a survey assessing SCT testing practices was administered to Canadian pediatric hospital transfusion laboratories serving hemoglobinopathy programs. RESULTS: Of 2268 donor RBC units tested, one was positive for SCT (0.04% [95% CI: 0.01%-0.24%]), at a cost of $19,384.56 CAD. The impact of SCT unit incorporation on lost HbS reduction was modest (Δ1%-3% [automated erythrocytapheresis] and Δ4%-15% [top-up/partial manual exchange]). The survey (with all 13 sites responding) showed variable SCT testing practice; four (31%) do not test, four (31%) test for children with SCD, and six (46%) test for neonates. CONCLUSION: RBC SCT testing may be more costly than beneficial or necessary in children with SCD. As of 2019, our transfusion service has ceased SCT testing for this population. Further research in the fetal/neonatal populations is needed to overturn this entrenched practice.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".