Antibiotics taken for other illnesses and spontaneous clearance of Helicobacter pylori infection in children
Bibliographic record
Abstract
PURPOSE: Factors that determine persistence of untreated Helicobacter pylori (H. pylori) infection in childhood are not well understood. We estimated risk differences for the effect of incidental antibiotic exposure on the probability of a detected clearance at the next test after an initial detected H. pylori infection. METHODS: The Pasitos Cohort Study (1998-2005) investigated predictors of H. pylori infection in children from El Paso, Texas, and Juarez, Mexico. Children were screened for infection at 6-month target intervals from 6 to 84 months of age, using the 13C-urea breath test corrected for body-size-dependent variation in CO2 production. Exposure was defined as courses of any systemic antibiotic (systemic) or those with anti-H. pylori action (HP-effective) reported for the interval between initial detected infection and next test. Binomial regression models included country of residence, mother's education, adequacy of prenatal care, age at infection, and interval between tests. RESULTS: Of 205 children with a test result and antibiotic data following a detected infection, the number of children who took > or =1 course in the interval between tests was 74 for systemic and 33 for HP-effective. The proportion testing negative at the next test was 66% for 0 courses, 72% for > or =1 systemic course, and 79% for > or =1 HP-effective course. Adjusted risk differences (95%CI) for apparent clearance, comparing > or =1 to 0 courses were 10% (1-20%) for systemic and 11% (0-21%) for HP-effective. CONCLUSIONS: Incidental antibiotic exposure appears to influence the duration of childhood H. pylori infection but seems to explain only a small portion of spontaneous clearance.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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".