Exposure to <i>Helicobacter pylori</i>–positive Siblings and Persistence of <i>Helicobacter pylori</i> Infection in Early Childhood
Bibliographic record
Abstract
OBJECTIVES: Cross-sectional studies suggest that Helicobacter pylori may be transmitted between siblings. The present study aimed to estimate the effect of an H pylori-infected sibling on the establishment of a persistent H pylori infection. MATERIALS AND METHODS: The authors used data collected from a Texas-Mexico border population from 1998 to 2005 (the "Pasitos Cohort Study"). Starting at age 6 months, H pylori and factors thought to be associated with H pylori were ascertained every 6 months for participants and their younger siblings. Hazard ratios were estimated from proportional hazards regression models with household-dependent modeling. RESULTS: Persistent H pylori infection in older siblings always preceded persistent infection in younger siblings. After controlling for mother's H pylori status, breast-feeding, antibiotic use, and socioeconomic factors, a strong effect was estimated for persistent H pylori infection in an older sibling on persistent infection in a younger sibling (hazard ratio 7.6, 95% confidence interval 1.6-37], especially when the difference in the age of the siblings was less than or equal to 3 years (hazard ratio 16, 95% confidence interval 2.5-112). CONCLUSIONS: These results suggest that when siblings are close in age, the older sibling may be an important source of H pylori transmission for younger siblings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".