A literature review evaluating the differences in mortality rates in urban versus rural patients with infective endocarditis in the United States of America
Bibliographic record
Abstract
Introduction: Over the last two decades, the prevalence of infective endocarditis in the United States of America has increased. This has been largely due to the increased life expectancy, increased rate of cardiac surgeries and the intravenous drug-use epidemic. Overall, the mortality rates related to infective endocarditis are decreasing. However, there are no literature reviews assessing mortality rate differences between urban and rural patients with infective endocarditis. Objective: Review the literature to identify any differences in mortality rates between urban and rural patients with infective endocarditis in the United States of America, between 1999 and 2019. Methods: A PubMed database search was conducted using key terms “infective endocarditis”, “rural”, “non-urban” and “urban”. Filtering for “humans” and “publication within the last 10 years”, yielded 69 results, three of which were appropriate for inclusion in the study. Two other studies were obtained from a search on Google Scholar. Results: Despite an inconsistency in definitions for terms “urban” and “rural”, the literature suggests there is a slightly higher mortality rate in rural compared to urban patients with infective endocarditis in the United States of America between 1999 and 2019. Furthermore, there appears to be an increasing mortality rate in rural patients with infective endocarditis, compared to a declining mortality rate in urban patients with infective endocarditis. Conclusion: The results from this literature review suggest a slightly higher mortality rate in rural compared to urban patients with infective endocarditis, but further research is needed to determine the cause of these differences.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.020 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".