The Trend of In-Hospital Epilepsy and Its Mortality in the USA: A National Analysis During 1997 - 2014
Bibliographic record
Abstract
Background: This study focuses on the trend of prevalence of epilepsy hospital discharges in the USA, and the aim is to find any change in the trend of prevalence of epilepsy in the USA and its in-hospital mortality. Methods: A retrospective cohort study used the Healthcare Cost and Utilization Project (HCUP) national database to analyze trends of epilepsy outcomes between 1997 and 2014. Results: A total of 4,594,213 total epilepsy discharges were documented between 1997 and 2014 in the HCUP database. The prevalence of annual discharges increased significantly during the study period from 209,002 discharges in 1997 to 280,255 in 2014 (P < 0.0001). There were a total of 35,643 in-hospital deaths due to epilepsy within the cohort during the study period. Mortality of epilepsy decreased across the entire cohort and between genders. In 1997, there were 2,256 documented in-hospital deaths, while 1,759 were recorded in 2014 (P = 0.00157). Conclusions: Our data showed that the prevalence of hospital epilepsy discharges is increasing in recent years; however, the in-hospital mortality is decreasing. The increase in the prevalence could be due to better detection or increased population, while the improved mortality could be due to better available treatments in recent years. Focusing on designing more accurate and affordable screening tools, as well as targeting further pharmacology-based treatments are an area of research that requires further investigation. J Neurol Res. 2020;10(1):3-6 doi: https://doi.org/10.14740/jnr563
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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 | 0.003 | 0.001 |
| 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 | 0.000 |
| 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".