Implementing newborn screening for sickle cell disease in Korle Bu Teaching Hospital, Accra: Results and lessons learned
Bibliographic record
Abstract
BACKGROUND: Early diagnosis of sickle cell disease (SCD) through newborn screening (NBS) is a cost-effective intervention, which reduces morbidity and mortality. In sub-Saharan Africa (SSA) where disease burden is greatest, there are no universal NBS programs and few institutions have the capacity to conduct NBS. We determined the feasibility and challenges of implementing NBS for SCD in Ghana's largest public hospital. PROCEDURE: The SCD NBS program at Korle Bu Teaching Hospital (KBTH) is a multiyear partnership between the hospital and the SickKids Center for Global Child Health, Toronto, being implemented in phases. The 13-month demonstration phase (June 2017-July 2018) and phase one (November 2018-December 2019) focused on staff training and the feasibility of universal screening of babies born in KBTH. RESULTS: During the demonstration phase, 115 public health nurses and midwives acquired competency in heel stick for dried blood spot sampling. Out of 9990 newborns, 4427 babies (44.3%) were screened, of which 79 (1.8%) were identified with presumptive SCD (P-SCD). Major challenges identified included inadequate nursing staff to perform screening, shortage of screening supplies, and delays in receiving screening results. Strategies to overcome some of the challenges were incorporated into phase one, resulting in increased screening coverage to 83.7%. CONCLUSIONS: Implementing NBS for SCD in KBTH presented challenges with implications on achieving and sustaining universal NBS in KBTH and other settings in SSA. Specific steps addressing these challenges comprehensively will help build on the modest initial gains, moving closer toward a sustainable national NBS program.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".