Prevalence, Morphological Classification, and Factors Associated With Severe Anemia among Children Under 5 Years of Age at Itojo Hospital
Bibliographic record
Abstract
study sought to establish the prevalence, morphological classifications, and factors associated with severe anemia among children attending Itojo Hospital. A hospital-based cross-sectional study design was used in this study in which children aged less than 5 years who attended the pediatric ward at Itojo Hospital were involved. Patients were consecutively recruited until a sample size of 296 was obtained. Data were collected from patients’ caregivers with a structured questionnaire. Data analysis was done using SPSS Version 20.0. Descriptive statistics and bivariate and multivariate logistic regression were used during data analysis. Multiple logistic regression models were used to show the strength of the relationship and the likelihood that each of the factors would lead to severe anemia among children under 5 years. Of the 296 patients enrolled, the prevalence of severe anemia was 13.9%. The Majority of the patients (50.7%) had microcytic anemia, followed by 32.8% with normocytic anemia. Factors that were significantly associated with severe anemia were the age of the child (P=0.029), HIV/AIDS (P=0.000), leukemia (P=0.000), and sickle cell disease (P=0.000). The prevalence of severe anemia among children less than five years of age was found to be relatively high hence increasingly becoming a public health problem. There is a need for age-specific interventions that comprehensively address the issue of improved nutrition, prevention, and management of HIV infection as well as chronic and genetic disorders.
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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".