The wider perspective: Barriers and recommendations for transfusion support for patients with sickle cell disease in low‐ and middle‐income countries
Bibliographic record
Abstract
Globally, sickle cell disease (SCD) is the most common inherited haemoglobinopathy. The highest burden of SCD is encountered in low- and middle-income countries (LMICs), most of which lack the resources to contend with the disease. There is a marked divide between care for individuals with SCD in high-income countries (HICs) versus LMICs, whereby the few disease-modifying therapies and curative regimens are only accessible to those in HICs. As such, blood transfusion remains central to the emergent treatment and prevention of complications of SCD. However, there are a myriad of related challenges in LMICs, which have impeded efforts to treat patients with SCD effectively. In addition to blood safety and availability, examples that impact SCD specifically include capabilities to detect and/or manage red blood cell alloimmunization, capacity for automated red cell exchange, limited immunohematology, suboptimal quality oversight with a lack of safeguards to prevent transfusion of incompatible blood and limited or absent post-transfusion surveillance to detect and/or manage transfusion-associated adverse events. Consequently, clinical practices that are otherwise regarded as standard of care in HICs remain the exception in LMICs, highlighting disparities in care. A multifaceted approach that prioritizes transfusion support in LMICs is needed to improve care for patients with SCD.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".