Evaluation of the Nutritional and Hematological Status of Sickle Cell Children Monitored in the Pediatric Department of the University Hospital Center of Yalgado Ouedraogo
Bibliographic record
Abstract
Objective: To assess the nutritional and hematological status of sickle cell children followed in the department of pediatrics of the Yalgado Ouédraogo University Hospital Centre (CHU-YO). Methodology: This was a cross-sectional study conducted from September 1, 2017, to February 28, 2018. All children with major sickle cell syndrome followed in the department of pediatrics at the CHU-YO and following their follow-up appointments were included in the study. Results: We included 230 children aged 11 months to 16 years with an average age of 8.5 years. The sex M/F ratio was 1.09. The SC heterozygotes were the most represented with 56.52%. The average hemoglobin level was 9.39 g/dl. The prevalences of wasting, stunting and underweight were respectively 23.04%, 15.65%, and 13.89%. In univariate analysis, the factors associated with emaciation was hyperleukocytosis (p=0.002).The factors associated with stunting were leukocytosis (p=0.01), severe anemia (p=0.01), SS phenotype (p=0.002), age range of 5-10 years (p=0.007), Secondary (P=0.007) and higher level (p=0.001) of father’s education, secondary (p=0.027) and higher level (p=0.034)of mothers’education , farmer(p=0.003) trader (p=0.042), and informal occupation of father (p = 0.002),and breastfeeding duration after 24 months (p=0.006). For underweight associated factors in univariate analysis were SS phenotype (p=0.003) and severe anemia (p=0.01). Conclusion: The prevalence of different types of malnutrition deficiency of sickle cell children followed at CHU-YO was high. It is important to strengthen the nutritional monitoring of children with sickle cell disease for better management of the disease.
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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".