Sickle hemoglobinopathy research in Zimbabwe and Zambia (SHAZ): Protocol for setting up an international Sickle Cell Disease registry
Bibliographic record
Abstract
Abstract Of the 500 000 children born with sickle cell disease annually, most cases occur in Africa, contributing to significant morbidity and mortality associated with limited sickle cell disease (SCD) health outcomes data and reduced access to therapeutic plus preventive care. We aim to develop and manage a standardized electronic SCD registry, establish consistent standards of care (SoC) for patients, improve the SCD research and biobanking capacity in Zimbabwe and Zambia. This five-year program employs mixed methods that include infrastructure and skilled manpower capacity building of SCD clinics, registry, biobanking, cohort and implementation science research studies to improve SCD treatment outcomes. We are collaborating with the SickleInAfrica consortium (Ghana, Mali, Nigeria, Tanzania, Uganda, and South Africa), the African Institute of Biomedical Sciences and Technology (AiBST) and St Jude’s Children Research Hospital. We established the SCD registry in Zimbabwe and Zambia for children and adult patients enrolling 1796/4000 (45%) participants to date. We are participating in SickleInAfrica consortium research activities, training health workers and educating SCD patient communities on SoC. This collaboration with African researchers, policymakers, health workers, and SCD patient communities will improve uptake of SCD SoC and increase our research capacity.
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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 | 0.069 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.075 | 0.015 |
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".