Incidence of Duodenal Ulcers and Gastric Ulcers in a Western Population: Back to Where It Started
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: As recently as 40 years ago, a decline in the incidence of peptic ulcers was observed. The discovery of Helicobacter pylori had a further major impact on the incidence of ulcer disease. Our aim was to evaluate the trends in the incidence and bleeding complications of ulcer disease in the Netherlands. METHODS: From a computerized endoscopy database of a district hospital, the data of all patients who underwent upper gastrointestinal endoscopy from 1996 to 2005 were analyzed. The incidence of duodenal and gastric ulcers, with and without complications, were compared over time. RESULTS: Overall, 20,006 upper gastrointestinal endoscopies were performed. Duodenal ulcers were diagnosed in 696 (3.5%) cases, with signs of bleeding in 158 (22.7%). Forty-five (6.5%) of these ulcers were classified as Forrest I and 113 (16.2%) as Forrest II. Gastric ulcers were diagnosed in 487 cases (2.4%), with signs of bleeding in 60 (12.3%). A Forrest 1 designation was diagnosed in 19 patients (3.9%) and Forrest 2 in 41 patients (8.4%). The incidence of gastric ulcers was stable over time, while the incidence of duodenal ulcers declined. CONCLUSIONS: The incidence of duodenal ulcer disease in the Dutch population is steadily decreasing over time. Test and treatment regimens for H pylori have possibly contributed to this decline. With a further decline in the prevalence of H pylori, the incidence of gastric ulcers is likely to exceed the incidence of duodenal ulcers in the very near future, revisiting a similar situation that was present at the beginning of the previous century.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| 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".