A simplified machine learning model utilizing platelet-related genes for predicting poor prognosis in sepsis.

Background: Thrombocytopenia is a known prognostic factor in sepsis, yet the relationship between platelet-related genes and sepsis outcomes remains elusive. We developed a machine learning (ML) model based on platelet-related genes to predict poor prognosis in sepsis. The model underwent rigorous evaluation on six diverse platforms, ensuring reliable and versatile findings.

A combined multilevel factor analysis and covariance regression model with mixed effects in the mean and variance structure.

Li et al developed a multilevel covariance regression (MCR) model as an extension of the covariance regression model of Hoff and Niu. This model assumes a hierarchical structure for the mean and the covariance matrix. Here, we propose the combined multilevel factor analysis and covariance regression model in a Bayesian framework, simultaneously modeling the MCR model and a multilevel factor analysis (MFA) model.

A phase 1 dose-escalation study of the poly(ADP-ribose) polymerase inhibitor senaparib in Australian patients with advanced solid tumors.

Background: Senaparib is a novel, selective poly(ADP-ribose) polymerase-1/2 inhibitor with strong antitumor activity in preclinical studies. This first-in-human, phase 1, dose-escalation study examined the safety and preliminary efficacy of senaparib in patients with advanced solid tumors.

Methods: Patients with advanced solid tumors were enrolled from three centers in Australia, using a conventional 3 + 3 design.

Strain-tuning of edge magnetism in zigzag graphene nanoribbons.

Using the determinant quantum Monte-Carlo method, we elucidate the strain tuning of edge magnetism in zigzag graphene nanoribbons. Our intensive numerical results show that a relatively weak Coulomb interaction may induce a ferromagnetic-like behaviour with a proper strain, and the edge magnetism can be enhanced greatly as the strain along the zigzag edge increases, which provides another way to control graphene magnetism even at room temperature.

Phase I Dose-Escalation Study of Ramucirumab in Chinese Patients with Advanced Solid Tumors.

Lessons Learned: Ramucirumab was well tolerated in Chinese patients with advanced solid tumors, and adverse events were manageable in this study.Pharmacokinetics characteristics in Chinese patients were similar to those in other populations. Immunogenicity was not detected.

Organization of Hospital Nursing, Provision of Nursing Care, and Patient Experiences With Care in Europe.

This study integrates previously isolated findings of nursing outcomes research into an explanatory framework in which care left undone and nurse education levels are of key importance. A moderated mediation analysis of survey data from 11,549 patients and 10,733 nurses in 217 hospitals in eight European countries shows that patient care experience is better in hospitals with better nurse staffing and a more favorable work environment in which less clinical care is left undone. Clinical care left undone is a mediator in this relationship.

Bayesian Multilevel MIMIC Modeling for Studying Measurement Invariance in Cross-group Comparisons.

Background: Recent methodological advancements should catalyze the evaluation of measurement invariance across groups, which is required for conducting meaningful cross-group comparisons.

Objective: The aim of this study was to apply a state-of-the-art statistical method for comparing latent mean scores and evaluating measurement invariance across managers' and frontline workers' ratings of the organization of hospital care.

Methods: On the 87 nursing units in a single institution, French-speaking and Dutch-speaking nursing unit managers' and staff nurses' ratings of their work environment were measured using the multidimensional 32-item practice environment scale of the nursing work index (PES-NWI).

A multivariate multilevel Gaussian model with a mixed effects structure in the mean and covariance part.

A traditional Gaussian hierarchical model assumes a nested multilevel structure for the mean and a constant variance at each level. We propose a Bayesian multivariate multilevel factor model that assumes a multilevel structure for both the mean and the covariance matrix. That is, in addition to a multilevel structure for the mean we also assume that the covariance matrix depends on covariates and random effects.

Levels and correlates of implicit rationing of nursing care in Swiss acute care hospitals--a cross sectional study.

Objectives: (1) To describe the levels of implicit rationing of nursing care in Swiss acute care hospitals; (2) to explore the associations between nine selected potential rationing predictors and implicit rationing of nursing care.

Design: Cross sectional multi-center study.

Settings: A quota sample of 35 acute care hospitals from the German, French and Italian speaking regions of Switzerland participating in RN4CAST (Registered Nurse Forecasting) study.

Group-level impact of work environment dimensions on burnout experiences among nurses: a multivariate multilevel probit model.

Background: Nurses' work environments are associated with burnout experiences among nurses. The RN4CAST project provides data on these constructs within a four-level structure (nurse, nursing unit, hospital, and country), implying more complicated multilevel analysis strategies than have been used in previous efforts studying this relationship.

Objectives: First, to explore and investigate the effect of the nursing unit, hospital, and country level variability on the relationship between dimensions of nurses' work environment and dimensions of burnout.

A multi-country perspective on nurses' tasks below their skill level: reports from domestically trained nurses and foreign trained nurses from developing countries.

Background: Several studies have concluded that the use of nurses' time and energy is often not optimized. Given widespread migration of nurses from developing to developed countries, it is important for human resource planning to know whether nursing education in developing countries is associated with more exaggerated patterns of inefficiency.

Objectives: First, to describe nurses' reports on tasks below their skill level.

Logistic random effects regression models: a comparison of statistical packages for binary and ordinal outcomes.

Background: Logistic random effects models are a popular tool to analyze multilevel also called hierarchical data with a binary or ordinal outcome. Here, we aim to compare different statistical software implementations of these models.

Methods: We used individual patient data from 8509 patients in 231 centers with moderate and severe Traumatic Brain Injury (TBI) enrolled in eight Randomized Controlled Trials (RCTs) and three observational studies.