The implementation of an emergency nursing framework (HIRAID) reduces patient deterioration: A multi-centre quasi-experimental study

Introduction Timely recognition and treatment of acutely ill patients at appropriate levels of the health system are fundamental to the quality and safety of healthcare. This study determines if the implementation of an emergency nursing framework HIRAID (History, Identify Red flags, Assessment, Interventions, Diagnostics, communication and reassessment) improves patient safety. Methods A quasi-experimental cohort study was conducted in two emergency departments in [Anonymised], Australia. HIRAID was implemented using a multi-pronged behaviour change intervention. Data of 920 patients (374 pre and 546 post) who deteriorated within 72-hours of ED departure were collected. Statistical tests were conducted as two-sided, with a 95% confidence interval to determine pre/post cohort association. Results Patients in the post group had more comorbidities, but experienced less deterioration associated with care delivered in the ED (27% to 13%). There was a reduction in treatment delays [ 28.3% to 15.1%, p = 0.041, 95% CI (1.1%–25.3%)], and delay or failure to escalate care when abnormal vital signs were identified [20.2% to6.9%, p = 0.014, 95% CI (3.5%–23.1%)]. Isolated nursing-related causal factors decreased from 20 (21%) to 6 (8%). Conclusions Implementing a standardised emergency nursing framework is associated with a reduction in clinical deterioration related to emergency care

The use of Raman spectroscopy to differentiate between different prostatic adenocarcinoma cell lines

Raman spectroscopy (RS) is an optical technique that provides an objective method of pathological diagnosis based on the molecular composition of tissue. Studies have shown that the technique can accurately identify and grade prostatic adenocarcinoma (CaP) in vitro. This study aimed to determine whether RS was able to differentiate between CaP cell lines of varying degrees of biological aggressiveness. Raman spectra were measured from two well-differentiated, androgen-sensitive cell lines (LNCaP and PCa 2b) and two poorly differentiated, androgen-insensitive cell lines (DU145 and PC 3). Principal component analysis was used to study the molecular differences that exist between cell lines and, in conjunction with linear discriminant analysis, was applied to 200 spectra to construct a diagnostic algorithm capable of differentiating between the different cell lines. The algorithm was able to identify the cell line of each individual cell with an overall sensitivity of 98% and a specificity of 99%. The results further demonstrate the ability of RS to differentiate between CaP samples of varying biological aggressiveness. RS shows promise for application in the diagnosis and grading of CaP in clinical practise as well as providing molecular information on CaP samples in a research setting

The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning

The White House Executive Order on Artificial Intelligence highlights the risks of large language models (LLMs) empowering malicious actors in developing biological, cyber, and chemical weapons. To measure these risks of malicious use, government institutions and major AI labs are developing evaluations for hazardous capabilities in LLMs. However, current evaluations are private, preventing further research into mitigating risk. Furthermore, they focus on only a few, highly specific pathways for malicious use. To fill these gaps, we publicly release the Weapons of Mass Destruction Proxy (WMDP) benchmark, a dataset of 3,668 multiple-choice questions that serve as a proxy measurement of hazardous knowledge in biosecurity, cybersecurity, and chemical security. WMDP was developed by a consortium of academics and technical consultants, and was stringently filtered to eliminate sensitive information prior to public release. WMDP serves two roles: first, as an evaluation for hazardous knowledge in LLMs, and second, as a benchmark for unlearning methods to remove such hazardous knowledge. To guide progress on unlearning, we develop RMU, a state-of-the-art unlearning method based on controlling model representations. RMU reduces model performance on WMDP while maintaining general capabilities in areas such as biology and computer science, suggesting that unlearning may be a concrete path towards reducing malicious use from LLMs. We release our benchmark and code publicly at https://wmdp.aiComment: See the project page at https://wmdp.a

Studies on Interaction at Sea

From about 7 years’ research work on the phenomenon of Ship Squat I have produced curves (Fig. 1) that will give predictions of maximum squat for merchant-ship types having block co-efficients, CB, ranging from 0.500 to 0.900. They are for vessels operating in ‘confined channel’ conditions and ‘open water’ conditions, i.e. those without breadth restrictions.</jats:p