Incidence of bleeding from gastroduodenal ulcers in patients with end-stage renal disease receiving hemodialysis
Bibliographic record
Abstract
BACKGROUND: Few large population-based studies have compared the incidence of bleeding of gastroduodenal ulcers between patients with and without end-stage renal disease. We investigated the association between ulcer bleeding and end-stage renal disease in patients receiving hemodialysis, and we sought to identify risk factors for ulcer bleeding. METHODS: We performed a nationwide seven-year population study using data from the National Health Insurance Research Database in Taiwan. We identified 36 474 patients with end-stage renal disease who were receiving hemodialysis, 6320 patients with chronic kidney disease and 36 034 controls matched for age, sex and medication use. We performed log-rank testing to analyze differences in survival time without ulcer bleeding among the three groups. We performed Cox proportional hazard regressions to evaluate the risk factors for ulcer bleeding among the three groups and to identify risk factors in patients receiving hemodialysis. RESULTS: Patients receiving hemodialysis and those with chronic kidney disease had a significantly higher incidence of ulcer bleeding than controls had (p<0.001). Hemodialysis (hazard ratio [HR] 5.24, 95% confidence interval [CI] 4.67-5.86) and chronic kidney disease (HR 1.95, 95% CI 1.62-2.35) were independently associated with an increased risk of ulcer bleeding. Diabetes mellitus, coronary artery disease, cirrhosis and use of nonsteroidal anti-inflammatory drugs were risk factors for ulcer bleeding in patients with end-stage renal disease who were receiving hemodialysis INTERPRETATION: Patients with end-stage renal disease who are receiving hemodialysis had a high risk of ulcer bleeding. Diabetes mellitus, coronary artery disease, cirrhosis and the use of nonsteroidal anti-inflammatory drugs were important risk factors for ulcer bleeding in these patients.
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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 | 0.000 |
| 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".