Trends of Upper Gastrointestinal Bleeding Mortality in the United States Before and During the COVID-19 Era: Estimates From the Centers for Disease Control WONDER Database
Bibliographic record
Abstract
Background: There have been reports of increased upper gastrointestinal bleeding (UGIB) in patients with coronavirus disease 2019 (COVID-19). Still, only a few studies have examined the mortality rate associated with UGIB in the United States before and during COVID-19. Hereby, we explored the trends of UGIB mortality in the United States before and during COVID-19. The study's objective was to investigate whether the COVID-19 pandemic significantly impacted UGIB mortality rates in the USA. Methods: The decedents with UGIB were included. Age-standardized mortality rates were estimated with the indirect method using the 2000 US Census as the standard population. We utilized the deidentified data from the Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research (CDC WONDER) database. Linear regression analysis was performed to determine 2021 projected mortality rates based on trends between 2012 and 2019 to quantify the association of the pandemic with UGIB-related deaths. Results: The mortality rate increased from 3.3 per 100,000 to 4.3 per 100,000 among the population between 2012 and 2021. There was a significant increase in the overall mortality rate between each year and the following year from 2012 to 2019, ranging from 0.1 to 0.2 per 100,000, while the rise in the overall mortality rate between each year and 2021 ranges from 0.4 to 0.9 per 100,000. Conclusions: Our results showed that the mortality rate increased among the population between 2012and 2021, suggesting a possible influence of COVID-19 infection on the incidence and mortality of UGIB.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".