Fecal pH as a marker of stunting among children hospitalized for diarrhea and other non-diarrheal pathologies
Bibliographic record
Abstract
Background: Fecal pH is a simple, non-invasive diagnostic tool used for initial screening of certain gastrointestinal (GI) diseases. Increased fecal pH indicates reduction of beneficial microbiota in the gut, which has emerged as a key factor contributing to stunting. The purpose of this study was to investigate the association of fecal pH with stunting in hospitalized children. Methods: This cross-sectional study was conducted on 200 children aged 06-24 months getting admitted in icddr,b Dhaka Hospital with diarrhea and Dhaka Shishu Hospital for other non-diarrheal pathologies. Length-for-age Z scores (LAZ) was measured and data on factors affecting linear growth was recorded. Fecal pH was measured on freshly collected stool samples following standard procedure. Multivariate linear regression was performed to explore relationship between fecal pH and LAZ scores. Results: The mean fecal pH of diarrheal and non-diarrheal children was 5.54±0.98 and 5.95±0.76, respectively. After inclusion of factors affecting linear growth into the regression model, a statistically significant inverse association between fecal pH and LAZ scores was observed in non-diarrheal children (p<0.01). However, such association did not apply for diarrheal children. Conclusion: Increased fecal pH in non-diarrheal children was found to have significant association with stunted growth, making fecal pH a possible indirect determinant of childhood stunting. However, no such associations were observed in case of diarrheal 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".