Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Gaussian Processes and Neural Modeling: an Asymptotic Analysis

Aiming at the construction of non-Markov models for single neuron's activity, the asymptotic behavior of the upcrossing first passage time probability density function through certain time-varying boundaries, is established for a class of stationary Gaussian processes. The goodness of the theoretical results is then tested, in specific instances, by means of a simulation method implemented on a large scale parallel compute

Computer-aided simulations of Gaussian processes and related asymptotic properties (Extended Abstract)

It has often been pointed out that first-passage-time (FPT) probability density functions (pdf's) through generally time-dependent boundaries play an essential role for the stochastic description of the behavior of various biological systems. The present paper is the natural extension of some investigations carried out for the class of one-dimensional diffusion processes admitting steady state densities in the presence of single asymptotically constant boundaries or of single asymptotically periodic boundarie

Estimating upcrossing FPT densities via simulation of Gaussian processes

The first passage time problem through a monotone boundary is considered for stationary Gaussian processes possessing rational spectral densities. Using a parallel procedure to simulate sample paths, estimates of the upcrossing probability density function of the first passage time are constructed. A comparison is provided with the numerical results obtained by Kostyukov via numerical solution of a Volterra integral equation. Finally, the effect of covariance's oscillatory components and of the behaviour of the boundary on the shape of the upcrossing FPT densities are pinpointe

Simulations of Gaussian Processes for Neuronal Modeling

The research work outlined in the present note highlights the essential role played by the simulation procedures implemented by us on CINECA supercomputers to complement the mathematical investigations carried within our group over the past several years. The ultimate target of our research is the understanding of certain crucial features of the information processing and transmission by single neurons embedded in complex networks. More specifically, here we provide a bird's eye look of some numerical and simulation results on the behavior of first passage time densities for Gaussian processes, both of a Markov and of a non-Markov type. Several figures indicate significant similarities or diversities between computational and simulated result

Computational Methods for the Evaluation of Neuron's Firing Densities

Some analytical and computational methods are outlined, that are suitable to determine the upcrossing first passage time probability density for some Gauss-Markov processes that have been used to model the time course of neuron's membrane potential. In such a framework, the neuronal firing probability density is identified with that of the first passage time upcrossing of the considered process through a preassigned threshold function. In order to obtain reliable evaluations of these densities, ad hoc numerical and simulation algorithms are implemente

Parallel simulations in FPT problems for Gaussian processes

The main results of our research related to first passage time (FPT) problems for stationary Gaussian processes are synthetically outlined. The vectorized and parallel algorithm, efficiently implemented on CRAY-T3E in FORTRAN90-MPI, allows to simulate a large number of sample paths of Gaussian stochastic processes in order to obtain reliable estimates of probability density functions (pdf) of first passage times through pre-assigned boundaries. The class of Gaussian processes characterized by damped oscillatory covariance functions and by Butterworth-type covariances have been extensively analyzed in the presence of constant and/or periodic boundaries. The analysis based on our simulation procedure has been particularly profitable as it has proved to provide an efficient research tool in all cases of interest to us when closed-form results or analytic evaluations were not available. Last but not least, in some cases it has allowed us to conjecture certain general features of FPT densities that successively have been rigorously prove

Evaluation of upcrossing first passage time densities for Gaussian processes via a simulation procedure

Upcrossing first passage time problems play a relevant role in various applied contexts and, in particular, in neuronal modeling. We consider the class of stationary normal processes characterized by damped oscillatory covariances. In order to evaluate the upcrossing FPT density, the initial values of the sample paths are taken according to preassigned probability densitie