Journal Article Reply Get access David Katz David Katz Department of Internal Medicine, Section of Infectious Diseases, St. Joseph Mercy Hospital, Ann Arbor, Michigan Search for other works by this author on: Oxford Academic PubMed Google Scholar The Journal of Infectious Diseases, Volume 154, Issue 3, September 1986, Pages 535–536, https://doi.org/10.1093/infdis/154.3.535-a Published: 01 September 1986
Primary testicular lymphoma is a rare testicular neoplasm that mainly affects elderly patients, with Human Immunodeficiency Virus (HIV) being a known risk factor in the younger population. Approximately 20% of patients will have disseminated disease with extra-nodal involvement at clinical presentation. Rarely, direct spread along the spermatic cord and gonadal vessels can occur and has been described in the literature. We present two cases of this phenomenon where the primary testicular tumour has spread along the gonadal vein to its origin at the inferior vena cava.
Abstract Background and objectives The Elixhauser Comorbidity Model is a prominent, freely-available risk adjustment model which performs well in predicting outcomes of inpatient care. However, because it relies solely on diagnosis codes, it may not capture the full extent of patient complexity. Our objective was to enhance and validatethe Elixhauser Model by incorporating additional clinical and demographic data to improve the accuracy of outcome prediction. Methods This retrospective observational cohort study included 55,945 admissions to the internal medicine service of a large tertiary care hospital in Jerusalem. A model was derived and validated to predict four primary outcomes. The four primary outcomes measured were length of stay (LOS), in-hospital mortality, readmission within 30 days, and increased care. Results Initially, the Elixhauser Model was applied using standard Elixhauser definitions based on diagnosis codes. Subsequently, clinical variables such as laboratory test results, vital signs, and demographic information were added to the model. The expanded models demonstrated improved prediction compared to the baseline model. For example, the R 2 for log LOS improved from 0.101 to 0.281 and the c-statistic to predict in-hospital mortality improved from 0.711 to 0.879. Conclusions Adding readily available clinical and demographic data to the base Elixhauser model improves outcome prediction by a considerable margin. This enhanced model provides a more comprehensive representation of patients’ health status. It could be utilized to support decisions regarding admission and to what setting, determine suitability for home hospitalization, and facilitate differential payment adjustments based on patient complexity.
Understand the constructs of the Python programming language and use them to build data science projects
Key Features
Learn the basics of developing applications with Python and deploy your first data application
Take your first steps in Python programming by understanding and using data structures, variables, and loops
Delve into Jupyter, NumPy, Pandas, SciPy, and sklearn to explore the data science ecosystem in Python
Book Description
Python is the most widely used programming language for building data science applications. Complete with step-by-step instructions, this book contains easy-to-follow tutorials to help you learn Python and develop real-world data science projects. The “secret sauce” of the book is its curated list of topics and solutions, put together using a range of real-world projects, covering initial data collection, data analysis, and production.
This Python book starts by taking you through the basics of programming, right from variables and data types to classes and functions. You'll learn how to write idiomatic code and test and debug it, and discover how you can create packages or use the range of built-in ones. You'll also be introduced to the extensive ecosystem of Python data science packages, including NumPy, Pandas, scikit-learn, Altair, and Datashader. Furthermore, you'll be able to perform data analysis, train models, and interpret and communicate the results. Finally, you'll get to grips with structuring and scheduling scripts using Luigi and sharing your machine learning models with the world as a microservice.
By the end of the book, you'll have learned not only how to implement Python in data science projects, but also how to maintain and design them to meet high programming standards.
What you will learn
Code in Python using Jupyter and VS Code
Explore the basics of coding – loops, variables, functions, and classes
Deploy continuous integration with Git, Bash, and DVC
Get to grips with Pandas, NumPy, and scikit-learn
Perform data visualization with Matplotlib, Altair, and Datashader
Create a package out of your code using poetry and test it with PyTest
Make your machine learning model accessible to anyone with the web API
Who this book is for
If you want to learn Python or data science in a fun and engaging way, this book is for you. You'll also find this book useful if you're a high school student, researcher, analyst, or anyone with little or no coding experience with an interest in the subject and courage to learn, fail, and learn from failing. A basic understanding of how computers work will be useful.
Abstract Optical spectroscopy and scanning tunneling microscopy are used to study the size and shape dependence of the electronic states in CdSe quantum rods. Samples having average rod dimensions ranging from 10 to 60 nm in length and 3.5 to 7 nm in diameter, with aspect ratios varying between 3 to 12, were investigated. Both size‐selective optical spectroscopy and tunneling spectra on single rods show that the level structure depends primarily on the rod diameter and not on length. With increasing diameter, the band gap and the excited state level spacings shift to the red. The level structure is assigned using a multi‐band effective‐mass model. We shall also discuss the effect of single electron charging on the tunneling spectra, possibly reflecting the quantum rod level degeneracy, and its dependence on the tunneling junction parameters.
We summarize our correlated scanning tunnelling microscopy and optical spectroscopy investigations of the electronic level structure and single-electron charging effects in CdSe quantum rods. Both optical and tunnelling spectra show that the level structure depends primarily on rod diameter and not on length. With increasing diameter, the bandgap and the excited state level spacings shift to the red. The level structure is assigned using a multi-band effective-mass model. The tunnelling spectra also exhibit, depending on the tunnel-junction parameters, single-electron charging effects that yield information on the degeneracy of the electronic states.
Abstract Background In Israel, internal medicine admissions are currently reimbursed without accounting for patient complexity. This is at odds with most other developed countries and has the potential to lead to market distortions such as avoiding sicker patients. Our objective was to apply a well-known, freely available risk adjustment model, the Elixhauser model, to predict relevant outcomes among patients hospitalized on the internal medicine service of a large, Israeli tertiary-care hospital. Methods We used data from the Shaare Zedek Medical Center, a large tertiary referral hospital in Jerusalem. The study included 55,946 hospitalizations between 01.01.2016 and 31.12.2019. We modeled four patient outcomes: in-hospital mortality, escalation of care (intensive care unit (ICU) transfer, mechanical ventilation, daytime bi-level positive pressure ventilation, or vasopressors), 30-day readmission, and length of stay (LOS). We log-transformed LOS to address right skew. As is usual with the Elixhauser model, we identified 29 comorbid conditions using international classification of diseases codes, clinical modification, version 9. We derived and validated the coefficients for these 29 variables using split-sample derivation and validation. We checked model fit using c-statistics and R 2 , and model calibration using a Hosmer–Lemeshow test. Results The Elixhauser model achieved acceptable prediction of the three binary outcomes, with c-statistics of 0.712, 0.681, and 0.605 to predict in-hospital mortality, escalation of care, and 30-day readmission respectively. The c-statistic did not decrease in the validation set (0.707, 0.687, and 0.603, respectively), suggesting that the models are not overfitted. The model to predict log length of stay achieved an R 2 of 0.102 in the derivation set and 0.101 in the validation set. The Hosmer–Lemeshow test did not suggest issues with model calibration. Conclusion We demonstrated that a freely-available risk adjustment model can achieve acceptable prediction of important clinical outcomes in a dataset of patients admitted to a large, Israeli tertiary-care hospital. This model could potentially be used as a basis for differential payment by patient complexity.
BACKGROUND: The transport of the inpatients to and from locations inside the hospital can vary in complexity depending on the patient location, status, and logistical needs. Most transport systems have not developed at the same speed as other medically related technologies. We conducted a pilot study of a new automated transport system for patients within the hospital. METHODS: Our innovative system was introduced in January 2020. We present a retrospective case review of all in-patient transport request during April 15, 2020 through May 30, 2020 at the Shaare Zedek Medical Center, Jerusalem, Israel. The system is fully automated and works via smartphone and electronic medical record integration. Transfer requests are processed on the basis of priority, proximity, and availably. RESULTS: During the study period there were 15, 581 transfer requests. Mean times to hospital destinations ranged from 9:25 to 28:02 minutes. Overall, mean times were quicker for emergency and surgical services. Trip times by priority code were likely influence by unmeasured confounders. There were no reported patient identification adverse events. Peak requests occurred during 0900-1500, and at the beginning of the week. CONCLUSION: Our automated in-patient transfer system appears to be efficient, safe, well received, and capable of servicing our large tertiary care medical center. Future controlled studies are needed to assess efficacy, adverse events, and clinical outcomes.
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