Background: Thousands of neurosurgical emergencies are transferred yearly to tertiary care facilities to assume a higher level of care. Several studies have examined how neurosurgical transfers influence patient outcomes, but characteristics of potentially avoidable transfers have yet to be investigated.
Objective: To identify whether potentially avoidable transfers represent a significant portion of transfers to a tertiary neurosurgical facility.
Methods: In this cohort study, we evaluated 916 neurosurgical patients transferred to a tertiary care facility over a 2-year period. Transfers were classified as potentially avoidable when no neurosurgical diagnostic test, intervention, or intensive monitoring was deemed necessary (n = 180). The remaining transfers were classified as justifiable (n = 736). The main outcomes and measures were age, sex, diagnosis, insurance status, intervention, distance of transfer, length of hospital and intensive care unit stay, mortality, discharge disposition, and cost.
Results: Nearly 20% of transfers were identified as being potentially avoidable. Although some of these patients had suffered devastating, irrecoverable neurological insults, many had innocuous conditions that did not require transfer to a higher level of care. Justifiable transfers tend to involve patients with nontraumatic intracranial hemorrhage and cranial neoplasm. Both groups were admitted to the intensive care unit at the same rate (approximately 70% of patients). Finally, the direct transportation cost of potentially avoidable transfers was $1.46 million over 2 years.
Conclusion: This study identified the frequency and expense of potentially avoidable transfers. There is a need for closer examination of the clinical and financial implications of potentially avoidable transfers.
Abbreviations: CI, confidence intervalIQR, interquartile rangeJT, justifiable transferOR, odds ratioPAT, potentially avoidable transferUAB, University of Alabama at Birmingham.
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http://dx.doi.org/10.1227/NEU.0000000000001378 | DOI Listing |
PLoS Comput Biol
January 2025
Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China.
Transfer learning aims to integrate useful information from multi-source datasets to improve the learning performance of target data. This can be effectively applied in genomics when we learn the gene associations in a target tissue, and data from other tissues can be integrated. However, heavy-tail distribution and outliers are common in genomics data, which poses challenges to the effectiveness of current transfer learning approaches.
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View Article and Find Full Text PDFACS Appl Mater Interfaces
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The replacement of the thermodynamically unfavorable anodic oxygen evolution reaction (OER) with a more favorable organic oxidation reaction, such as the anodic oxidation of benzylamine, has garnered significant interest in hybrid water electrolyzer cells. This approach promises the production of value-added chemicals alongside hydrogen fuel generation, improving overall energy efficiency. However, achieving high current density for benzylamine oxidation without interference from OER remains a challenge, limiting the practical efficiency of the electrolyzer cell.
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