Micronutrients (including zinc) reduce diarrhoea in children: The Pakistan Sprinkles Diarrhoea Study
Bibliographic record
Abstract
AIMS: To examine the effect of the daily use of micronutrients (including zinc) or the same micronutrients plus heat inactivated lactic acid bacteria (LAB), on diarrhoea in children compared to placebo. METHODS: A triple blind randomised clinical trial in an urban slum of Karachi, Pakistan. Micronutrients (including zinc), micronutrients (including zinc and LAB), or placebo, were provided daily for two months to 75 young children (aged 6-12 months) identified at high risk for diarrhoea related mortality on the basis of history of at least one episode of diarrhoea in the preceding two weeks. The longitudinal prevalence of diarrhoea was defined as the percentage of days a child had diarrhoea out of the days the child was observed. RESULTS: Mean longitudinal prevalence of diarrhoea in the micronutrient-zinc group was 15% (SD = 10%) child-days compared to 26% (SD = 20%) child-days in the placebo group and 26% (SD = 19%) child-days in the micronutrient-zinc-LAB group. The difference between the micronutrient-zinc-LAB and placebo groups was not significant. CONCLUSION: The daily provision of micronutrients (including zinc) reduces the longitudinal prevalence of diarrhoea and thus may also reduce diarrhoea related mortality in young children; heat inactivated LAB has negative effects in these children.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".