Seizures With Major Comorbidity and Complications: Association of the Teaching Status of the Hospitals With the Outcomes
Bibliographic record
Abstract
Background: This study aims to compare the outcomes of complicated seizure cases in teaching institutions as compared to non-teaching hospitals. Methods: A retrospective cohort study was conducted utilizing the Healthcare Cost and Utilization Project (HCUP) national database to analyze outcomes of seizures between 2012 and 2016 in the USA. Results: We evaluated 267,430 of seizure patients with major complication or comorbidity between 2012 and 2016. Of these, 6,980 in-hospital deaths were reported. There was a trend toward a significantly higher mortality in teaching compared with non-teaching hospitals (P = 0.07). The average length of stay (LOS) was 5.2 days, with LOS in 2014 and 2016, being longer in teaching hospitals (P < 0.05). Hospital charges were not significantly different among the two groups, but both types of hospitals did show a statistically significant charge increase from 2012 to 2016 (P < 0.001). Conclusions: Our data showed that there is a trend toward significantly higher mortality in teaching hospitals. LOS was also more reported in teaching hospitals, which could be inherent to the increased volume and coordination of care and more complexity of the cases in teaching hospitals. However, hospital charges were not different in teaching versus non-teaching hospitals. J Neurol Res. 2020;10(4):127-131 doi: https://doi.org/10.14740/jnr588
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".