Why Do Mortality Rates for Nonvariceal Upper Gastrointestinal Bleeding Differ around the World? A Systematic Review of Cohort Studies
Bibliographic record
Abstract
BACKGROUND: Discrepancies exist in reported mortality rates of nonvariceal upper gastrointestinal bleeding (NVUGIB). OBJECTIVE: To perform a systematic review assessing possible reasons for these disparate findings and to more reliably compare them. METHODS: The MEDLINE, EMBASE and ISI Web of Knowledge databases were searched for studies reporting mortality rates in NVUGIB involving adults and published in English. To ensure robust and contemporary estimates, studies spanning 1996 to January 2011 that included more than 1000 patients were selected. RESULTS: Eighteen of 3077 studies were selected. Ten studies used administrative databases and the remaining eight used registries. The mortality rates reported in these studies ranged from 1.1% in Japan to 11% in Denmark. There were variations in reported mortality rates among countries and also within countries. Reasons for these disparities included a spectrum of quality in reporting as well as heterogeneous definitions of case ascertainment, differing patient populations with regard to severity of presentation and associated comorbidities, varying durations of follow-up and different health care system-related practices. CONCLUSIONS: Wide differences in reported NVUGIB mortality rates are attributable to differences in adopted methodologies and populations studied. More uniform standards in reporting are needed; only then can true observed variations enable a better understanding of causes of death and pave the way to improved patient 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 | 0.020 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".