Average Delay Research Articles

Time interval between the onset of symptoms and diagnosis of multiple sclerosis and the influential factors: A national registry-based study

ObjectiveThe time to diagnosis of multiple sclerosis (MS) is of great importance for early treatment, thereby reducing the disability and burden of the disease. The purpose of this study was to determine the time from the onset of clinical symptoms to the diagnosis of MS and to evaluate the factors associated with a late diagnosis in Iranian MS patients. MethodsThe present cross-sectional study was conducted on patients with MS who were registered in the National MS Registry System of Iran (NMSRI). ResultsOverall, 23291 MS patients registered in 18 provinces of Iran were included in this study. The mean (standard deviation) interval between the onset of the disease and diagnosis of MS was 13.42 (32.40) months, and the median was one month. The diagnostic interval of 41.6% of patients was less than one month, and 14.8% of them had a one-month time to diagnosis. Patients with an age of onset below 18 years and those diagnosed after the age of 50 years had a longer time to diagnosis (P<0.001). Patients with primary progressive MS (PPMS) had the longest time to diagnose and those with relapsing-remitting MS (RRMS) had the shortest time (P<0.001). The results of negative binominal regression showed that the average rate of delay in diagnosis in women was 12% less than that in men. The average delay in diagnosis in patients with a positive family history of MS was 23% more than that in others. The rate of delay in the diagnosis of patients with PPMS and secondary progressive MS was 2.22 and 1.66 times higher, respectively, compared with RRMS. ConclusionThe findings of the present study revealed that more than half of the MS patients were diagnosed within a one-month interval from the symptom onset, which is an acceptable period. More attention should be paid to patients’ access to medical facilities and MS specialists.

On the value of advanced information about delayed rewards

In a variety of laboratory preparations, several animal species prefer signaled over unsignaled outcomes. Here we examine whether pigeons prefer options that signal the delay to reward over options that do not and how this preference changes with the ratio of the delays. We offered pigeons repeated choices between two alternatives leading to a short or a long delay to reward. For one alternative (informative), the short and long delays were reliably signaled by different stimuli (e.g., SS for short delays, SL for long delays). For the other (non-informative), the delays were not reliably signaled by the stimuli presented (S1 and S2). Across conditions, we varied the durations of the short and long delays, hence their ratio, while keeping the average delay to reward constant. Pigeons preferred the informative over the non-informative option and this preference became stronger as the ratio of the long to the short delay increased. A modified version of the Δ–Σ hypothesis (González et al., J Exp Anal Behav 113(3):591–608. https://doi.org/10.1002/jeab.595, 2020a) incorporating a contrast-like process between the immediacies to reward signaled by each stimulus accounted well for our findings. Functionally, we argue that a preference for signaled delays hinges on the potential instrumental advantage typically conveyed by information.

A novel routing method for dynamic control in distributed computing power networks

Driven by diverse intelligent applications, computing capability is moving from the central cloud to the edge of the network in the form of small cloud nodes, forming a distributed computing power network. Tasked with both packet transmission and data processing, it requires joint optimization of communications and computing. Considering the diverse requirements of applications, we develop a dynamic control policy of routing to determine both paths and computing nodes in a distributed computing power network. Different from traditional routing protocols, additional metrics related to computing are taken into consideration in the proposed policy. Based on the multi-attribute decision theory and the fuzzy logic theory, we propose two routing selection algorithms, the Fuzzy Logic-Based Routing (FLBR) algorithm and the low-complexity Pairwise Multi-Attribute Decision-Making (lPMADM) algorithm. Simulation results show that the proposed policy could achieve better performance in average processing delay, user satisfaction, and load balancing compared with existing works.

ASSESSMENT OF SIGNALIZED INTERSECTION: A CASE STUDY IN BAGHDAD City by USING SIDRA SOFTWARE

Over the world, signalized intersections and road networks with heavy traffic have posed severe issues. So, it is crucial to employ cutting-edge software tools to ensure the current system can be managed and assessed. Therefore; this paper aims to analyze and evaluate the traffic flow performance at AL-Masbah signalized intersections in Baghdad city during peak hours by utilizing SIDRA INTERSECTION 5. For purposes of evaluation, the Level of Service LOS, average delay time, capacity, degree of saturation, queue for Intersection, fuel consumption, and carbon dioxide emission have been considered. In this study, video count was used to collect data. The main input data included peak hour factor (PHF), numbers and width of lanes, percent of heavy vehicles for each direction and movement, median, phasing, and timing. The results of the analysis indicated that the intersection is operated at LOS ( F) with a backlog queue of 166.1 which is the queue for any lane used by movement (vehicles)and a 1262m queue for any lane used by movement (meters) m, while fuel consumption was at a rate of 849.9 liters per hour. Furthermore, the results revealed that carbon dioxide (CO2) emission was increased by 2124.8 Kg per hour. So, It can be concluded that the intersection needs to improve its level of service by changing its geometry or by implementing an Intelligent Transportation System (ITS).

Stability analysis of nonlinear systems with delayed impulses: a generalised average dwell-time scheme

This paper studies the input-to-state stability (ISS) and uniform stability for impulsive nonlinear systems with delayed impulses, where delays are flexible between two consecutive impulsive instants. Illustrating effects caused by delays, concepts of generalised average impulsive delay and generalised reverse average impulsive delay are introduced, and more applicable in practice. Moreover, sufficient conditions for stability properties are formulated with destabilising and stabilising impulses through a general candidate ISS-Lyapunov function and the generalised conditions. It is shown that the double effects of delays in impulses (i.e. stabilising an initial unstable system, or contrarily, destabilising an initial stable system) coincide with the existing results using the exponential candidate ISS-Lyapunov function. Finally, two examples are provided to demonstrate our developed techniques.

Patient centric factors affecting delay in self-diagnosis and seeking medical care for oral cancer: a cross-sectional study

Background: Oral cancer is often detected in the advanced stage, with a low survival rate of 5 years and with increase in mortality which, affects patient quality of life. The early diagnosis would reduce the mortality rate and morbidity rate, by bringing a change in the quality of life. Methods: The study utilized convenient sampling method to select the participants. Participants who were diagnosed with oral cancer currently at 3rd and 4th stage. Data collection was done using pre-validated self-administered questionnaire, which was provided to the participants through Google forms. Results: The study demonstrated 43.1% people diagnosed with stage-3 oral cancer had an average delay of 34.6 months and 26% of stage-4 cancer patient had an average delay of 59.9 months to their first visit to treatment center. In this study 34% of the study participants were unaware and had lack of knowledge about oral cancer related symptoms. Self-medications/herbal medications, fear for the treatment, socio-economic factors, COVID-19/ lockdown restriction were also other reasons reported by patients. Conclusions: The prompt treatment is necessary in both advanced cases and early cases of oral cancer. Radiotherapy, chemotherapy, and surgery are all part of the treatment when given timely. Thus, early diagnosis plays a vital role in saving many live.

Cluster stability-driven optimization for enhanced routing in heterogeneous vehicular networks

The new era of the Internet of Things is promoting the evolution of self-driving vehicles into connected and autonomous vehicles (CAVs). The deployment of CAVs in smart cities is highly dependent on the performance of their underlying networks known as vehicular networks. Designing an effective clustering approach is of great importance in such dynamic networks as it can significantly improve the reliability and scalability of the routing protocols. In this paper, we consider a heterogeneous vehicular network architecture that supports vehicle-to-vehicle and vehicle-to-infrastructure communications based on IEEE 802.11p and cellular networks (LTE/5G) with direct communications, namely cellular vehicle-to-everything (C-V2X) technologies. We introduce a novel clustering scheme for real-time routing based on the proposed network architecture. We formulate the problem of optimal clustering for connected and autonomous vehicles (OCCAV) and show that the problem is NP-hard. We then develop a clusters' stability maximization algorithm (CSM), which utilizes the stability degree of vehicles over a prediction horizon to efficiently solve the optimization problem in real-time. The algorithm is used within a rolling horizon framework for continuously solving the problem, making the resulting clusters adaptive to future traffic dynamics. We propose a hybrid routing protocol based on our clustering scheme, aiming to improve the packet delivery ratio and reduce the average delivery delay. For evaluation purposes, we use extensive realistic simulations based on mobility scenarios validated using real vehicular trajectories. The results demonstrate that our clustering scheme improves the alternative algorithms in terms of the average cluster head duration, cluster head change rate, cluster member duration, and overall stability by 61%, 62%, 44%, and 52%, respectively. CSM also outperforms clustering overhead by 54%. Compared to the other cluster-based routing, our scheme also achieves a higher packet delivery ratio by up to 30%, and a lower average delay by up to 52%. To provide an in-depth analysis of the optimality of our scheme and its alternatives, we also use the Gurobi optimizer to find an optimal solution to the OCCAV problem. The results suggest that our scheme can achieve near-optimal cluster stability.

Catastrophic complications following microvascular free tissue transfer: A 10-year review of NSQIP data

IntroductionThere is an absence of literature regarding the risks of catastrophic medical outcomes (CMOs) such as stroke, cardiac arrest, and pulmonary embolism in microvascular free tissue transfer. This study aims to determine the CMO and mortality rates, as well as risk factors, associated with microvascular reconstruction of the head and neck, extremity, and breast. MethodsThis study uses data from the American College of Surgeons National Surgical Quality Improvement Program. Cases of microvascular free tissue transfer from 2012 to 2021 were analyzed to assess the 30-day rates of CMOs, including death, as well as associated risk factors. ResultsOf the 22,839 included patients, 785 (3.44%) experienced 1043 CMOs, including 99 (0.43%) deaths. Pulmonary complications of prolonged respiratory failure and pulmonary embolism were the most common. Independent risk factors included age, male sex, underweight status, longer operation times, American Society of Anesthesiologists (ASA) class of III or above, wound classification other than clean, and underlying conditions such as diabetes, hypertension, chronic obstructive pulmonary disorder, dyspnea, metastatic cancer, and ventilator dependence. CMOs were associated with an average 10-day delay in hospital discharge. Multivariate regression analysis revealed that head and neck reconstructions were associated with increased risk of CMO (OR 4.96; p < 0.0001). ConclusionThis is the largest study to examine CMOs following microvascular free tissue transfer. Compared to previous literature spanning the period between 2006 and 2011, we observed a decreased rate of CMOs but a slight increase in 30-day mortality. Our data provide updated and comprehensive criteria for risk stratification and patient counseling. The modifiable risk factors reported in our study should be considered in elective, non-urgent cases of microvascular reconstruction.

Transit Signal Priority under Connected Vehicle Environment: Deep Reinforcement Learning Approach

Transit Signal Priority (TSP) is a traffic signal control strategy that can provide priority to transit vehicles and thus improve transit service and enhance transportation equity. Conventional TSP strategies often ignore the fluctuation of passenger occupancy in transit vehicles, leading to sub-optimal solutions for the entire system. The use of Connected Vehicle (CV) technology enables the adoption of a more fine-grained objective in optimizing traffic signals, such as person delay, by allowing real-time information on passenger occupancy to be obtained. In this study, a deep reinforcement learning algorithm, deep Q-network (DQN), is applied to develop a traffic signal controller that minimizes the average person delay. The proposed DQN controller is tested in a simulation environment modeled after a real-world intersection and compared with pretimed and actuated controllers. Results show that the proposed DQN controller has the best performance in terms of average person delay. Compared to the baseline, it reduces the average person delay by 18.77% in peak hours and 23.37% in off-peak hours. Furthermore, it also results in decreased average delays for both buses and cars. The sensitivity analysis results indicate that the proposed controller has the potential for practical applications, as it can effectively handle some dynamic changes.

IoT-based Facelook and Fingerprint Safe Security System

This research aims to develop an advanced safe security system by combining Facelook and Fingerprint technologies based on the Internet of Things (IoT). These technologies are expected to provide a higher level of security and facilitate access for safe owners. Subsequently, testing is carried out on the safe's opening mechanism after successful authentication via both the fingerprint sensor and the face recognition system. These trials encompass the evaluation of success rates, the speed of the opening mechanism, and the overall response time. Data from the testing phase is collected and analyzed to comprehensively assess the system's performance. The average notification delivery delay for the face recognition system was measured at 2.67 seconds with a standard deviation of 0.37. The notification delivery data revealed an average delay of 2.04 seconds with a standard deviation of 0.38 These findings collectively affirm the effectiveness of the integrated face recognition and fingerprint system in the proposed safe security setup

Stability of Traffic Equilibria in a Day-to-Day Dynamic Model of Route Choice and Adaptive Signal Control

Adaptive traffic signal control has the potential to promote the efficient use of road intersections, thus contributing to the effectiveness of urban traffic management schemes. However, the reaction of drivers to repeatedly updated signal settings and the ensuing route choice dynamics may trigger the emergence of various kinds of network instability. In this study, the joint evolution of traffic flows and adaptive signal settings in a road network is investigated at the level of day-to-day dynamics with an explicit focus on the stability issue. We show how a Logit form signal control policy can be used, in interaction with route choice, to counter the emergence of instabilities possibly arising as a consequence of various behavioral factors and network conditions. After providing a general formulation of the model as a discrete time, deterministic nonlinear dynamical system, an explicit analysis of fixed-point stability is carried out for a simple network. Numerical results obtained from the implementation of the model on two example networks are presented in order to support the analytical findings of this study. We conclude that, in an integrated traffic management and information system, a properly calibrated adaptive signal control policy has the potential to offset the destabilizing effect of highly accurate driver information supplied by navigational aids. Our findings also suggest that the Logit-like control policy performs better than the Equisaturation signal setting method, in terms of average intersection delay at equilibrium, for all levels of driver information and travel demand tested in the experiment.

Cooperative traffic signal control through a counterfactual multi-agent deep actor critic approach

Signal control has been effective to alleviate urban traffic congestion. Massive related works about signal timing optimization have been proposed and led to many signal control methods and systems. In recent years, reinforcement learning (RL) algorithms have attracted the increasing attention of researchers in the area of signal control optimization, since they can learn the optimal timing policy themselves by analyzing changing patterns between the traffic condition and signal timing plans. Multi-intersection traffic signal control problems face more challenges than classical single intersection problems such as the dimensionality issue as the joint action space of multiple agents grows exponentially with the number of intersections. The existing state-of-art multi-agent reinforcement learning algorithms developed for other areas may not suit well with traffic signal control given the complex spatial–temporal nature of traffic flows. In this paper, we propose a novel multi-agent counterfactual actor–critic with scheduler (MACS) framework for multiple interactions. In this method, decentralized actors control the traffic signals and the centralized critic combines recurrent policies with feed-forward critics. Additionally, a scheduler module that exchanges information among agents helps the individual agent better understand the entire environment. The proposed method was evaluated using simulation experiments based on a real-world urban street network in Shenzhen, China. Results showed that the proposed method outperforms the classic model-based method and several existing RL-based methods according to different measures such as queue length, average travel time delay, and throughput.

Investigation on distributed scheduling with lot-streaming considering setup time based on NSGA-II in a furniture intelligent manufacturing

Distributed flexible flowshop scheduling is getting more important in the large-scale panel furniture industry. It is vital for a higher manufacturing efficiency and economic profit. The distributed scheduling problem with lot-streaming in a flexible flow shop environment is investigated in this work. Furthermore, the actual constraints of packaging collaborative and machine setup times are considered in the proposed approach. The average order waiting time for packaging and average order delay rate is used as objectives. Non-dominated sorting method is used to handle this bi-objective optimization problem. An improved encoding method was proposed to address the large-scale orders that need to be divided into sub-lots based on genetic algorithm. The proposed approach is firstly validated by benchmark with other multi-objectives evolutionary algorithms. The results found that the proposed approach had a good convergence and diversity. Besides, the influence of the proportion of large-scale orders priority level and sub-lot size was investigated in a panel furniture manufacturing scenario. The results can be concluded that the enterprise could obtain shorter order average waiting time and delay rate when the sub-lot sizes were set as two and the order priority level was allocated in the proportion of 1:2:3:4:5.

Research on the Setting and Control Method of Centrosymmetric Short Straight Lanes for Signalized Intersections

A new traffic organization method has been proposed to address the issue of traffic congestion caused by oversaturation through traffic during peak hours at signalized intersections. This method involves the installation of approximately centrosymmetric short straight lanes (CSSLs) to facilitate the passage of through vehicles, aiming to improve the traffic efficiency at signalized intersections. Firstly, two types of intersection designs with CSSLs were investigated, along with their operational principles. Secondly, a capacity model for the CSSL was established, taking into account the probability theory and mutual blockage considerations, as well as a delay model based on vehicle accumulation under oversaturated conditions. Finally, the intersection of Nanxu Road and Huangshan South Road in Zhenjiang City was chosen as an example. A simulation scheme was designed using VISSIM software to evaluate the effectiveness of the proposed method by implementing CSSLs before and after the intersection. The simulation results indicate that this method effectively increases the capacity of the oversaturated intersection approaches at the intersection and reduces the average delay. Under different lengths of short through lanes and utilization rates, the capacity of the eastbound short through lane and its adjacent lane increased by 20.7%, and the average delay decreased by 14.2% in all cases. Furthermore, a sensitivity analysis was conducted to examine the impact of short through lane capacity and the number of short through lanes on intersection capacity under different traffic volume levels. This analysis provides further insights into the influence of the CSSL on intersection capacity.

Exponential Stability of Stochastic Time-Delay Neural Networks with Random Delayed Impulses

The mean square exponential stability of stochastic time-delay neural networks (STDNNs) with random delayed impulses (RDIs) is addressed in this paper. Focusing on the variable delays in impulses, the notion of average random delay is adopted to consider these delays as a whole, and the stability criterion of STDNNs with RDIs is developed by using stochastic analysis idea and the Lyapunov method. Taking into account the impulsive effect, interference function and stabilization function of delayed impulses are explored independently. The results demonstrate that delayed impulses with random properties take a crucial role in dynamics of STDNNs, not only making stable STDNNs unstable, but also stabilizing unstable STDNNs. Our conclusions, specifically, allow for delays in both impulsive dynamics and continuous subsystems that surpass length of impulsive interval, which alleviates certain severe limitations, such as presence of upper bound for impulsive delays or requirement that impulsive delays can only exist between two impulsive events. Finally, feasibility of the theoretical results is verified through three simulation examples.

Real-Time Adjustment Method for Metro Systems with Train Delays Based on Improved Q-Learning

This paper presents a solution to address the challenges of unexpected events in the operation of metro trains, which can lead to increased delays and safety risks. An improved Q-learning algorithm is proposed to reschedule train timetables via incorporating train detention and different section running times as actions. To enhance computational efficiency and convergence rate, a simulated annealing dynamic factor is introduced to improve action selection strategies. Additionally, importance sampling is employed to evaluate different policies effectively. A case study of Shenzhen Metro is conducted to demonstrate the effectiveness of the proposed method. The results show that the method achieves convergence, fast computation speed, and real-time adjustment capabilities. Compared to traditional methods such as no adjustment, manual adjustment, and FIFO (First-In-First-Out), the proposed method significantly reduces the average total train delay by 54% and leads to more uniform train headways. The proposed method utilizes a limited number of variables for practical state descriptions, making it well suited for real-world applications. It also exhibits good scalability and transferability to other metro systems.

Performance analysis, evaluation, and improvement of selected unsignalized intersection using SIDRA software – Case study

Abstract Significant social, economic, and environmental costs are associated with traffic congestion. The effectiveness of intersections makes a substantial contribution to the effectiveness of entire road networks. The three-leg at-grade Al-Husainea intersection, located 20 km from the center of holy Karbala city in Iraq, is subjected to serious congestion, resulting in an increase in delay time, reduction in capacity, and bad level of service (LOS). Therefore, it is essential to use advanced software tools to ensure that the current intersection can be controlled, evaluated, and improved. So, the major goal of this study is to use SIDRA, an acronym for signalized and unsignalized intersection design and research aid, software to evaluate the LOS of the Al-Husainea intersection, in which the traffic is assessed using the current LOS. LOS, delay time, and degree of saturation were the criteria utilized to evaluate the traffic flow performance. SIDRA is also used to assess benefits as a result of suggested changes in the design of particular junctions. The first stage is to gather field data regarding traffic volumes utilizing a method of traffic volume gathering. From 7 am to 6 pm, the SIDRA program gathered data for a full 7 days. The results showed that the LOS for the Al-Husainea intersection in the Al-Husainea arm is F, with an average delay of 52 s per vehicle and a saturation level of 0.86 v/c. Finally, it was determined that the Al-Husainea intersection needs additional improvements based on the study and findings from the SIDRA program, and some remedies are suggested in this study to improve the intersection traffic flow.

Cerebrotendinous Xanthomatosis patients with late diagnosed in single orthopedic clinic: two novel variants in the CYP27A1 gene

BackgroundCerebrotendinous Xanthomatosis (CTX) is a rare autosomal recessive lipid storage disorder caused by loss of function variants in the CYP27A1 gene which encodes sterol 27-hydroxylase, on chromosome 2q35. Although the symptoms begin commonly in infancy, CTX diagnosis is often delayed. The aim of this study is to review the orthopedic findings of the disease by providing an overview of the clinical features of the disease. It is to raise awareness of this condition for which early diagnosis and treatment are important.MethodsWe retrospectively evaluated the clinical, laboratory, radiological, and genetic findings of eight patients from four families who were admitted to our Orthopedics and Traumatology Department between 2017 and 2022 due to bilateral Achilles tendon xanthomas, were found to have high cholestanol and CYP27A1 gene mutations.ResultsThe mean age of patients was 37, and five of them were male. The mean age at the onset of symptoms was 9.25 years. The mean age of initial diagnosis was 33.75 years. Between symptom onset and clinical diagnosis, an average delay of 24.5 years was observed. All patients had bilateral Achilles tendon xanthoma. Notably, a novel variant (c.670_671delAA) in CYP27A1 gene was identified in three patients who also presented with peripheral neuropathy and bilateral pes cavus. One patient had osteoporosis and four patients had osteopenia. Five patients had a history of bilateral cataracts. Furthermore, three of the patients had early-onset chronic diarrhea and three of the patients had ataxia. Two of the patients had epilepsy and seven of the patients had behavior-personality disorder. All patients had low intelligence, but none of them had cardiac disease.ConclusionWe present the diagnostic process and clinical features which the largest CTX case series ever reported from single orthopedic clinic. We suggest that patients with normal cholesterol levels presenting with xanthoma being genetically analyzed by testing at their serum cholestanol level, and that all siblings of patients diagnosed with CTX be examined.

Kombinasi Algoritma Priority Scheduling dan Earliest Due Date untuk Sistem Penjadwalan Slitting Produk Berbasis Web

Production scheduling is one of the most critical components of production management that can impact efficiency and resource utilization. This research focuses on the development of a web-based production scheduling system that integrates two algorithms, Earliest Due Date (EDD) and Priority Scheduling. This research includes the analysis, design, implementation and testing of a those two algorithms in a web-based application for production management. The result of this research will be analyzed through the results of how both algorithms affects lateness and tardiness. The research findings indicate that the application can effectively manage Work Orders. The application can generate an optimal schedule when conditions arise where some tasks have higher priority than others by combining both algorithms. The system can identify potentially late tasks by calculating the tardiness of the given jobs. And from the analysis results, the application can generate an efficient production schedule with an on-time accuracy rate of 74%, and the average delay for each job is 0.95 hours.

(085) Long-Term Outcomes of Testosterone Replacement Therapy Delaying Biochemical Recurrence Post-RARP: Frequency-Matched Case-Control Study

Abstract Introduction Testosterone Replacement Therapy (TRT) is known to improve functional and sexual outcomes in hypogonadal men. TRT historically has been contraindicated for the management of hypogonadism in men with prostate cancer (PC). Objective In 2020, we demonstrated in a cohort of 850 men that TRT reduced biochemical recurrence (BCR) by 50% (median follow-up 3.5 yrs.). The present study extends the follow up to 5.25 years Methods A retrospective review of prospectively collected data between December 2009 and June 2018 undergoing robotic radical prostatectomy by a single surgeon was conducted. 152 patients were administered TRT post-RP with a calculated free testosterone (cFT; below the 25%) and were frequency matched to 420 patients not receiving TRT with respect to pathological stage (p-stage) and Gleason Grade Group (GGG). Biochemical recurrence (BCR) was defined as two consecutive prostate specific antigen (PSA) blood tests &amp;gt;0.2 ng/mL. An adjusted Kaplan-Meier (KM) graph was used to analyze rate and time to BCR. Results Baseline differences in demographics were observed in BMI (p = 0.006) and follow up time (p = 0.0023), and cFT (p = 0.0156). While overall BCR was not significantly different between the two groups, a difference was observed in time to recurrence. In adjusted KM analysis, patients with a higher calculated free testosterone were less likely to have a BCR (p = 0.05). In the same analysis, patients with a higher GGG, p-stage, and preoperative PSA (p &amp;lt;0.01) were more likely to have a BCR. Patients receiving TRT had similar rates of BCR (p = 0.676). However, if they recurred, there was an average delay of 1.4 years. Conclusions TRT was observed to have a continuing protective effect in preventing and delaying BCR in patients post-RARP when compared to patients who did not receive TRT. Disclosure Any of the authors act as a consultant, employee or shareholder of an industry for: Advisory board and speaker for Coloplast; consultant for Cynosure; advisory board and speaker for Halozyme; intellectual property with Masimo; advisory board for Promescent; consultant for Sprout; advisory board for Xialla.