Sociodemographic Determinants of Nutritional Status in Children with Sickle Cell Anaemia in Benin City Edo State, Nigeria
Bibliographic record
Abstract
Background: Nigeria has the highest burden of sickle cell anaemia (SCA) in Africa. Patients with SCA present with life-long complications in addition to growth failure. With comprehensive care, patients with SCA have improved survival and well-being, with some studies reporting overnutrition. This study aims to assess nutritional status and sociodemographic determinants in children with SCA. Methods: A descriptive cross-sectional study was carried out among children aged 1-16years attending the paediatric SCA clinics and their sex and age-matched controls. Anthropometric measurements were carried out to determine their nutritional status, and the data obtained were analyzed using SPSS software. The sociodemographic and relevant clinical information was obtained using a semi-structured questionnaire. Results: The majority (86%) of the children with SCA had normal height and weight, but the mean weight, weight for age z-score (WAZ), body mass index z-score (BMIZ) and height for age z-score (HAZ) were significantly lower than the HbAA controls. The mean WAZ was significantly higher in children from high socioeconomic class (SEC) and those whose mothers had tertiary education than those from lower educational statuses. Similarly, the mean HAZ was significantly higher in children from high SEC, tertiary maternal education and younger children. Conclusion: Most children with SCA have normal nutritional status despite significantly lower nutritional indicators compared to children with HbAA genotype. Higher educational status of mothers and SEC were associated with a significantly higher nutritional status among the subjects.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".