Renal status of children with sickle cell disease in Accra, Ghana.
Bibliographic record
Abstract
INTRODUCTION: In West Africa, the prevalence of sickle cell disease (SCD) is 2%. The disease adversely affects growth, development and organ function including the kidneys. There is however a dearth of information about the renal status of SCD children in Ghana. OBJECTIVES: To assess the renal status of children with SCD in steady state. DESIGN: A cross-sectional case-control study. SETTING: Paediatric Sickle Cell Clinic, Korle Bu Teaching Hospital, Accra. PARTICIPANTS: Cases-357 SCD cases and 70 of their HbAA siblings as controls. METHODS: Documentation of their socio-demographic data, clinical data and dipstick urinalysis findings, and renal ultrasonography on selected participants. RESULTS: The mean [SD] age was 7.18 [3.15]yrs for cases and 5.16[3.28]yrs for controls. The genotypes were Hb SS (76.7%), Hb SC (21.8 %), and Hb Sβthal (1.4%). Urinalysis showed leucocyturia in 12.6% versus 5.7% (χ2=62.5 and the p=0.000)), isolated proteinuria in 2.8% versus 1.43% (χ2=10.01 and p=0.001) haematuria in 2.6% versus 0% (χ2=9.233, p=0.002) and nitrites in 2.2% versus 1.4% (χ2=16.3,p=0.02) of cases and controls respectively. The youngest SCD case with proteinuria was 2 yrs. old. Proteinuria prevalence increased with age, , occurring in 5.7% of cases aged 9-11yrs. and 20.6% of cases aged 12 yrs. Two-thirds of the proteinuria cases were aged 9-12 yrs., of whom 50% were aged 12 yrs. Renal ultrasound findings were normal in all those examined. CONCLUSION: Urinary abnormalities suggesting nephropathy occur early in SCD patients in Ghana. Routine dipstick screening at clinic visits countrywide would help early detection and prompt intervention to limit renal impairment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".