Cross-sectional study exploring the prevalence and clinical manifestations of acute diarrhea among under-5 children in primary care hospital in Democratic Republic of the Congo
Bibliographic record
Abstract
Background: Acute diarrhea (AD), which is defined as frequent passing of liquid stools compared to normal, is a serious and worrying problem and remains a concern for healthcare systems because of its high mortality cause in children under 5 years old. The authors' study aimed to present the prevalence and to describe the clinical manifestation of AD among under-5 children. Methods: From June 2022 to May 2023, the authors conducted a retrospective, descriptive and cross-sectional study including all patients aged 0-5 years hospitalized for AD. Results: Out of 512 patients, only 197 (38.5%) children with AD were selected for our study. The average age is 25.5 months, and the sex ratio is 1.11. Some families (75.1%) have clean latrines, and 21.8% use water from the river. Inaccessibility to clean water and intolerance or food poisoning were the causes of acute diarrhea in children. Major signs and symptoms are fever, dehydration and vomiting. Weight loss and malnutrition are the major complications of AD in children. The treatment of AD is provided by oral rehydration solutions and antibiotics. Conclusion: The study highlights the significant prevalence of acute diarrhea among under-5 children underscores the importance of preventive measures and government intervention, such as the introduction of rotavirus vaccination. However, conclusions regarding prevalence rates should be interpreted with caution due to the lack of detailed population data.
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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".