Effect of sibling number in the household and birth order on prevalence of Helicobacter pylori: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Infection with Helicobacter pylori (H. pylori) is acquired mainly in childhood, with studies demonstrating this is related to living conditions. Effects of sibling number and birth order on prevalence of infection have not been extensively studied. METHODS: The authors performed a cross-sectional survey of adults, aged between 50 and 59 years, previously involved in a community-screening programme for H. pylori in Leeds and Bradford, UK. Prevalence of H. pylori was assessed at baseline with urea breath test. All individuals who were alive, and could be traced, were contacted by postal questionnaire in 2003 obtaining information on number of siblings and birth order. Data concerning childhood socioeconomic conditions were stored on file from the original study. RESULTS: 3928 (47%) of 8407 original participants provided data. Prevalence of infection increased according to sibling number (20% in those with none vs 63% with eight or more). Controlling for childhood socioeconomic conditions and birth order using multivariate logistic regression, infection odds were substantially increased with three siblings compared with none [odds ratio (OR) 1.51; 95% confidence interval (CI) 1.06-2.15], and a gradient of effect continued up to eight or more siblings (OR 5.70; 95% CI 2.92-11.14). Odds of infection also increased substantially with birth order, but the positive gradient disappeared on adjustment for sibling number and childhood socioeconomic conditions. CONCLUSIONS: : In this cross section of UK adults, aged 50-59 years, sibling number in the household, but not birth order, was independently associated with prevalence of H. pylori infection.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".