Sickle cell disease in children: an update of the evidence in low- and middle-income settings
Bibliographic record
Abstract
Sickle cell disease (SCD), one of the most common monogenetic diseases in the world, is associated with multisystemic complications that begin in childhood. Most of the babies homozygous for the sickle haemoglobin gene are born in sub-Saharan Africa. Over the years, progress has been made with early diagnosis through newborn screening, penicillin prophylaxis, pneumococcal immunisation, transcranial Doppler (TCD) screening, hydroxyurea therapy and chronic blood transfusions with remarkably improved survival and quality of life of children with SCD. However, wide disparities in outcomes exist between high-income countries (HICs) where over 90% survive to adulthood, and low-income and middle-income countries (LMICs) where less than half achieve that milestone. Even in HICs, racial inequities pose barriers to accessing specialised care and receiving treatment for acute pain episodes. Better understanding of SCD pathophysiology is being exploited to develop new disease-modifying drugs and gene therapy approaches to further improve outcomes. Bone marrow transplantation is established as a curative treatment for SCD, but it is largely unavailable in LMICs. To bridge the disparity and inequity gaps, innovative approaches are needed in LMICs. Validated and more affordable, easy-to-use point-of-care tests offer opportunities to link early diagnosis with immunisation programmes and healthcare encounters. Widespread use of hydroxyurea therapy-a relatively affordable and effective disease-modifying drug-in LMICs would help improve survival and quality of life. Integration of SCD treatment into primary care linked to district level/provincial hospitals that are supported with evidence-based guidelines will help extend needed interventions to many more patients living in LMICs.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".