Comparative outcomes in patients with ulcer‐ vs non‐ulcer‐related acute upper gastrointestinal bleeding in the United Kingdom: a nationwide cohort of 4474 patients
Bibliographic record
Abstract
BACKGROUND: Outcomes after Nonvariceal upper gastrointestinal bleeding (NVUGIB) have historically focused on ulcer-related causes. Little is known regarding non-ulcer bleeding, the most common cause of NVUGIB. AIM: To compare outcomes between ulcer- and non-ulcer-related NVUGIB and explore whether these could be explained by differences in baseline characteristics, bleeding severity or processes of care. METHODS: Analysis of 4474 patients with NVUGIB from 212 United Kingdom hospitals as part of a nationwide audit. Logistic regression models were used to adjust for baseline characteristics, bleeding severity and processes of care. RESULTS: 1682 patients had ulcer-related and 2792 patients had non-ulcer-related bleeding. Those with ulcer-related bleeding were older (median age 73 vs 69, P < 0.001), less likely to have been taking a PPI (18% vs 32%, P < 0.001), more likely to have been taking aspirin (40% vs 27%, P < 0.001) and present with shock (43% vs 32%, P < 0.001). Furthermore, those with ulcer-related bleeding were more likely to receive blood transfusion (66% vs 39%, P < 0.001), PPI infusion (27% vs 5%, P < 0.001) and endoscopic therapy (37% vs 8%, P < 0.001). Overall, ulcer-related bleeding had higher odds of in-hospital mortality (OR: 1.54; 95% CI: 1.21-1.96, P < 0.0001), rebleeding (OR: 2.08; 95% CI: 1.73-2.51, P < 0.0001) and need for surgical/radiologic intervention (OR: 2.64; 95% CI: 1.85-3.77, P < 0.0001). The associations disappeared after adjustment for bleeding severity, whereas adjustment for patient characteristics or process of care factors had no impact. CONCLUSION: Patients with ulcer-related NVUGIB bleeding have worse outcomes than those with non-ulcer-related NVUGIB bleeding, which is due to more severe bleeding.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".