<i>Helicobacter pylori</i>–positive Versus <i>Helicobacter pylori</i>–negative Idiopathic Peptic Ulcers in Children With Their Long‐term Outcomes
Bibliographic record
Abstract
OBJECTIVES: The aim of this study is to investigate the differences in the characteristics between Helicobacter pylori-positive and H pylori-negative primary ulcers in Chinese children. PATIENTS AND METHODS: We conducted a retrospective review of children with primary peptic ulcers. Demographic data, clinical presentations, endoscopic features, histological findings, H pylori prevalence, and ulcer recurrences were studied. RESULTS: Forty-three Chinese children with primary peptic ulcers were diagnosed over 8 years and were reviewed. There were 31 boys and 12 girls (median age 12 years, range 3-16 years). Thirty children (70%) presented with acute gastrointestinal bleeding, whereas only 19 had a history of epigastric pain. Twenty-three patients (53.5%) were H pylori positive. H pylori-positive ulcers developed in older children (median age 12 vs 10 years, P<0.05) and affected more males (91.3% vs 50%, P<0.01) than the H pylori-negative group. The annual ulcer recurrence rates were estimated to be 5.2% (95% CI 4.2-6.3) and 11.4% (95% CI 9.1-13.6) for positive and negative groups, respectively (P<0.05). Multivariate logistic regression suggested H pylori-negative status and ulcer size >1cm were indepen-dent risk factors for recurrence. CONCLUSIONS: Our report suggests that H pylori-negative primary ulcers exist in children with their own distinct features. In contrast to H pylori-positive ulcers, H pylori-negative ulcers develop in younger children, affect both sexes equally, and carry a higher recurrence risk.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".