Global burden of childhood diarrhea and interventions
Bibliographic record
Abstract
PURPOSE OF REVIEW: Diarrhea is a leading cause of morbidity and mortality among children under 5 years in low-income and middle-income countries. Over the past 2 decades under-five mortality has decreased substantially, but reductions have been uneven and unsatisfactory in resource-poor regions. RECENT FINDINGS: There are known interventions which can prevent diarrhea or manage children who suffer from it. Interventions with proven effectiveness at the prevention level include water, sanitation, and hygiene interventions, breastfeeding, complementary feeding, vitamin A and zinc supplementation, and vaccines for diarrhea (rotavirus and cholera). Oral rehydration solution, zinc treatment, continued feeding, and antibiotic treatment for certain strains of diarrhea (cholera, Shigella, and cryptosporidiosis) are effective strategies for treatment of diarrhea. The recent Lancet series using the 'Lives Saved' tool suggested that if these identified interventions were scaled up to a global coverage to at least 80%, and immunizations to at least 90%; almost all deaths due to diarrhea could be averted. SUMMARY: The current childhood mortality burden highlights the need of a focused global diarrhea action plan. The findings suggest that with proper packaging of interventions and delivery platforms, the burden of childhood diarrhea can be reduced to a greater extent. All that is required is greater attention and steps toward right direction.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".