Larger sample sizes are needed when developing a clinical prediction model using machine learning in oncology: methodological systematic review.

Background: Having a sufficient sample size is crucial when developing a clinical prediction model. We reviewed details of sample size in studies developing prediction models for binary outcomes using machine learning (ML) methods within oncology and compared the sample size used to develop the models with the minimum required sample size needed when developing a regression-based model (N).

Methods: We searched the Medline (via OVID) database for studies developing a prediction model using ML methods published in December 2022.

The Value of Clinical Prediction Models in General Practice: A Qualitative Study Exploring the Perspectives of People With Lived Experience of Depression and General Practitioners.

Introduction: Prediction models are increasingly being used to guide clinical decision making in primary care. There is a lack of evidence exploring the views of patients and general practitioners (GPs) in primary care around their use and implementation. We aimed to better understand the perspectives of GPs and people with lived experience of depression around the use of prediction models and communication of risk in primary care.

The multifunctional use of an aqueous battery for a high capacity jellyfish robot.

The batteries that power untethered underwater vehicles (UUVs) serve a single purpose: to provide energy to electronics and motors; the more energy required, the bigger the robot must be to accommodate space for more energy storage. By choosing batteries composed primarily of liquid media [e.g.

Mechanistic Understanding of Long Duration Fast Charge Aqueous Zinc Batteries Using Physically Adsorbed Oligomers as Interphases.

Polymers have been used as additives in the liquid electrolytes typically used for secondary batteries that utilize metals as anode. Such additives are conventionally argued to improve long-term anode performance by suppressing morphological and hydrodynamic instabilities thought to be responsible for out-of-plane and dendritic metal deposition during battery charging. More recent studies have reported that the polymer additives provide even more fundamental mechanisms for stabilizing metal electrodeposition through their ability to regulate metal electrodeposit crystallography and, thereby, morphology.