Incidence, Predictive Factors, Clinical Characteristics and Outcome of Non-variceal Upper Gastrointestinal Bleeding – A Prospective Population-based Study from Hungary
Bibliographic record
Abstract
BACKGROUND AND AIMS: Acute non-variceal upper gastrointestinal bleeding (UGIB) is associated with significant morbidity and mortality. Our aim was to evaluate the incidence, management, risk factors and outcomes of acute non-variceal UGIB in a population-based study from Hungary. METHODS: The present prospective one-year study involved six major community hospitals in Western Hungary covering a population of 1,263,365 persons between January 1 and December 31, 2016. Data collection included demographics, comorbidities endoscopic management, Glasgow-Blatchford score (GBS), Rockall score (RS) transfusion requirements, length of hospital stay and mortality. RESULTS: 688 cases of acute non-variceal UGIB were included with an incidence rate of 54.4 (95%CI: 50.5-58.6) per 100,000 per year. Endoscopy was performed within 12 hours in 71.8%. 5.3% of the patients required surgical treatment and the overall mortality was 13.5%. Weekend presentation was associated with increased transfusion requirements (p=0.047), surgery (p=0.016) and mortality (p=0.021). Presentation with hemodynamic instability or presence of comorbidities was associated with transfusion (p<0.001 both), second look endoscopy (p<0.001 both), re-bleeding (p<0.001 both), longer in-hospital stay (p<0.001 both) and mortality (p=0.017 and p<0.001). GBS was associated with transfusion requirement (AUC:0.82; cut-off: GBS >7points), while mortality was best predicted by the post-endoscopic RS (AUC:0.75; cut-off: RS >5points). CONCLUSIONS: Incidence rates of acute non-variceal UGIB in Western Hungary are in line with international trends. Longer pre-hospital time, comorbidities, hemodynamic instability, weekend presentation, treatment with anticoagulants or non-steroidal anti-inflammatory drugs was associated with worse outcomes.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 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".