The Effects of Probiotic and Prebiotic Administration in Children with Acute Diarrhea at Day-Care Centers
Bibliographic record
Abstract
Prevention of diarrhea needs an appropriate immune system supported by normal microbiota composition. This study aimed to determine whether probiotic or prebiotic enriched Growing-Up Milk could significantly reduce incidence of acute diarrhea. The randomized, double-blind, placebo-controlled clinical study was conducted in Surabaya, Gresik, and Sidoarjo cities, East Java–Indonesia, between July 2007 and January 2008. This study involved healthy children aged 1–5 years at day-care centers and were randomized to receive three different Growing-Up Milk containing probiotic, prebiotic, or placebo groups (containing neither probiotic nor prebiotic). The day-care staff and parents reported the amount of milk consumed, symptoms, and duration of acute diarrhea during the observation time. A total of 162 participants were divided into probiotic (55), prebiotic (54), and placebo groups (53). The incidence of diarrhea in all the participants was 1.2%, which was the least incidence from the prebiotic group and the highest in the placebo group and significantly different (P = 0.001). The mean duration of diarrhea in all the intervention groups was lower than the placebo group, although neither was statistically nor clinically significant (P = 0.254). Administration of Growing-Up Milk enriched with probiotics or prebiotics appears to be a great opportunity in reducing the incidence of acute diarrhea in children aged 1–5 years.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".