Natural History of <i>Helicobacter pylori</i> Infection in Mexican Schoolchildren
Bibliographic record
Abstract
OBJECTIVES: The aim of the present study was to estimate the incidence and spontaneous clearance rate of Helicobacter pylori infection and the effect of some variables on these outcomes in schoolchildren. METHODS: From May 2005 to December 2010, 718 schoolchildren enrolled in 3 public boarding schools in Mexico City participated in the follow-up. At the beginning of the study and every 6 months thereafter, breath samples were taken to detect H pylori infection; blood samples and anthropometric measurements were taken to evaluate nutritional status. Data on sociodemographic characteristics were collected. RESULTS: The prevalence of H pylori infection was 38%. The incidence rate was 6.36%/year. Schoolchildren with anemia or iron deficiency at the beginning of the study (who received iron supplements) showed a higher infection acquisition rate than those with normal iron nutritional status, hazard ratio (HR) 12.52 (95% confidence interval [CI] 4.01%-39.12%), P < 0.001 and HR 2.05 (95% CI 1.09%-3.87%), P = 0.027, respectively. The spontaneous clearance rate of the infection was 4.74%/year. The spontaneous clearance rate was higher in children who had iron deficiency (who received iron supplements), HR 5.02 (95% CI 1.33%-18.99%), P = 0.017, compared with those with normal nutritional iron status. It was lower in schoolchildren with ≥ 2 siblings compared with schoolchildren with 1 or no siblings, HR 0.23 (95% CI 0.08%-0.63%), P = 0.004. CONCLUSIONS: H pylori infection status is dynamic in schoolchildren. Variables related to health status and infection transmission, such as iron status and number of siblings, are important for the incidence and spontaneous clearance 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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".