Site specific incidence rate of genomic subtypes of enteropathogenic Escherichia coli and association with enteric inflammation and child growth
Bibliographic record
Abstract
There is a lack of information highlighting the possible association between the genomic subtypes of enteropathogenic Escherichia coli (EPEC) on environmental enteric dysfunction (EED) and on linear growth during childhood. Genomic subtypes of EPEC from stool samples collected from 1705 children enrolled in the MAL-ED birth cohort were detected by TaqMan Array Cards. We measured site-specific incidence rate by using Poisson regression models, identified the risk factors and estimated the association of genomic subtypes of EPEC with the composite EED score and linear growth at 24 months of age. In general, the highest incidence rate (39%) was found among children having aEPEC infection, which was the greatest in Tanzania (54%). Exclusive breastfeeding and having an improved sanitation facility were found to be protective factors against EPEC infection. In the multivariate models, in overall effect after adjusting for the potential covariates aEPEC showed strong positive associations with the EED scores and tEPEC showed a positive association with poor linear growth at 24 months of age. Our analyses may lay the cornerstone for a prospective epidemiologic investigation for a potential vaccine development aimed at reducing the burden of EPEC infections and combat childhood malnutrition.
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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 | 0.003 |
| 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 | 0.000 |
| 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".