Epidemiology of Infective Endocarditis in Rural Upstate New York, 2011 - 2016
Bibliographic record
Abstract
BACKGROUND: The epidemiology of infective endocarditis (IE) depends on a number of host factors whose prevalence can vary globally. The usual patient population affected by IE is sicker and older, often with many comorbid conditions. The risk is growing in younger populations due to the emerging epidemic of intravenous (IV) drug use. We have performed a temporal trend analysis of various factors of IE in the rural counties covering a major part of central Upstate New York. METHODS: We performed a retrospective analysis of electronic medical records of patients who were admitted in a tertiary care hospital in rural Upstate New York and diagnosed with IE from January 1, 2011 to December 31, 2016. Forty-five patients were identified with definite IE and nine with possible IE. RESULTS: Total incidence of IE was 3.5 cases per 100,000 person years in the total population and 4.4 if we consider total population ≥ 18 years in the denominator. A significant (P = 0.022) increase in incidence of IE from 2011 to 2016 was seen by univariate analysis. Incidence was higher in males (P = 0.029) and for those aged 65 or older (P = 0.0003). IV drug use among cases is noted to be more prevalent in 2015 and 2016 compared to previous years. CONCLUSION: In this study of patients in a rural region of New York, an increase in the incidence of IE was seen over the study period with changes in patient characteristics and etiology over this time. We speculate that an increase in IV drug use could be a leading factor in the recent and future increased incidence of IE in the area.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".