Symptomatic gallstone disease: Recurrence patterns and risk factors for relapse after first admission, the RELAPSTONE study.

BACKGROUND: Delayed cholecystectomy in patients with symptomatic gallstone disease is associated with recurrence. Limited data on the recurrence patterns and the factors that determine them are available. OBJECTIVE: We aimed to determine the pattern of relapse in each symptomatic gallstone disease (acute pancreatitis, cholecystitis, cholangitis, symptomatic choledocholithiasis, and biliary colic) and determine the associated factors. METHODS: RELAPSTONE was an international multicenter retrospective cohort study. Patients (n = 3016) from 18 tertiary centers who suffered a first episode of symptomatic gallstone disease from 2018 to 2020 and had not undergone cholecystectomy during admission were included. The main outcome was relapse-free survival. Kaplan-Meier curves were used in the bivariate analysis. Multivariable Cox regression models were used to identify prognostic factors associated with relapses. RESULTS: Mean age was 76.6 [IQR: 59.7-84.1], and 51% were male. The median follow-up was 5.3 months [IQR 2.1-12.4]. Relapse-free survival was 0.79 (95% CI: 0.77-0.80) at 3 months, 0.71 (95% CI: 0.69-0.73) at 6 months, and 0.63 (95% CI: 0.61-0.65) at 12 months. In multivariable analysis, older age (HR = 0.57; 95% CI: 0.49-0.66), sphincterotomy (HR = 0.58, 95% CI: 0.49-0.68) and higher leukocyte count (HR = 0.79; 95% CI: 0.70-0.90) were independently associated with lower risk of relapse, whereas higher levels of alanine aminotransferase (HR = 1.22; 95% CI: 1.02-1.46) and multiple cholelithiasis (HR = 1.19, 95% CI: 1.05-1.34) were associated with higher relapse rates. CONCLUSION: The relapse rate is high and different in each symptomatic gallstone disease. Our independent predictors could be useful for prioritizing patients on the waiting list for cholecystectomies.

A field evaluation of a matching mechanism: University applicant behavior in Australia.

The majority of undergraduate university applications in the state of New South Wales-Australia's largest state-are processed by a clearinghouse, the Universities Admissions Centre (UAC). Applicants submit an ordered list of degrees to the UAC, which applies a matching algorithm to allocate university places to eligible applicants. Applicants receive advice on how to construct their degree preference list from multiple sources including individual universities. This advice is often confusing and misleading. To evaluate the performance of the current system, we run a large sample (832 observations) online experiment with experienced participants in a choice environment that mimics the UAC application process, and in which truth telling is optimal. We vary the advice received across treatments: no advice, the UAC advice only, an instance of misleading university advice only, and both the UAC and the misleading university advice together. Overall, 75.5% of participants fail to behave in their best interest. High rates of applicant manipulation persist even when applicants are provided with the UAC's accurate advice. Students who attend nonselective government high schools are more prone to use strictly dominated strategies than those who attend academically selective government high schools and private high schools.

Modelling discomfort: How do drivers feel when cyclists cross their path?

Many cyclist fatalities occur on roads when crossing a vehicle path. Active safety systems address these interactions. However, the driver behaviour models that these systems use may not be optimal in terms of driver acceptance. Incorporating explicit estimates of driver discomfort might improve acceptance. This study quantified the degree of discomfort experienced by drivers when cyclists crossed their travel path. Participants were instructed to drive through an intersection in a fixed-base simulator or on a test track, following the same experimental protocol. During the experiments, three variables were controlled: 1) the car speed (30, 50 km/h), 2) the bicycle speed (10, 20 km/h), and 3) the bicycle-car encroachment sequence (bicycle clears the intersection first, potential 50 %-overlap crash, and car clears the intersection first). For each trial, a covariate, the car's time-to-arrival at the intersection when the bicycle appears (TTAvis), was calculated. After each trial, the participants were asked to report their experienced discomfort on a 7-point Likert scale ranging from no discomfort (1) to maximum discomfort (7). The effect of the three controlled variables and the effect of TTAvis on drivers' discomfort were estimated using cumulative link mixed models (CLMM). Across both experimental environments, the controlled variables were shown to significantly influence discomfort. TTAvis was shown to have a significant effect on discomfort as well; the closer to zero TTAvis was (i.e., the more critical the situation), the more likely the driver reported great discomfort. The prediction accuracies of the CLMM with all three controlled variables and the CLMM with TTAvis were similar, with an average accuracy between 40 and 50 % for the exact discomfort level and between 80 and 85 % allowing deviations by one step. Our model quantifies driver discomfort. Such model may be included in the decision-making algorithms of active safety systems to improve driver acceptance. In fact, by tuning system activation times depending on the expected level of discomfort that a driver would experience in such situation, a system is not likely to annoy a driver.

Forward collision warning based on a driver model to increase drivers' acceptance.

Objective: Systems that can warn the driver of a possible collision with a vulnerable road user (VRU) have significant safety benefits. However, incorrect warning times can have adverse effects on the driver. If the warning is too late, drivers might not be able to react; if the warning is too early, drivers can become annoyed and might turn off the system. Currently, there are no methods to determine the right timing for a warning to achieve high effectiveness and acceptance by the driver. This study aims to validate a driver model as the basis for selecting appropriate warning times. The timing of the forward collision warnings (FCWs) selected for the current study was based on the comfort boundary (CB) model developed during a previous project, which describes the moment a driver would brake. Drivers' acceptance toward these warnings was analyzed. The present study was conducted as part of the European research project PROSPECT ("Proactive Safety for Pedestrians and Cyclists"). Methods: Two warnings were selected: One inside the CB and one outside the CB. The scenario tested was a cyclist crossing scenario with time to arrival (TTA) of 4 s (it takes the cyclist 4 s to reach the intersection). The timing of the warning inside the CB was at a time to collision (TTC) of 2.6 s (asymptotic value of the model at TTA = 4 s) and the warning outside the CB was at TTC = 1.7 s (below the lower 95% value at TTA = 4 s). Thirty-one participants took part in the test track study (between-subjects design where warning time was the independent variable). Participants were informed that they could brake any moment after the warning was issued. After the experiment, participants completed an acceptance survey. Results: Participants reacted faster to the warning outside the CB compared to the warning inside the CB. This confirms that the CB model represents the criticality felt by the driver. Participants also rated the warning inside the CB as more disturbing, and they had a higher acceptance of the system with the warning outside the CB. The above results confirm the possibility of developing wellsaccepted warnings based on driver models. Conclusions: Similar to other studies' results, drivers prefer warning times that compare with their driving behavior. It is important to consider that the study tested only one scenario. In addition, in this study, participants were aware of the appearance of the cyclist and the warning. A further investigation should be conducted to determine the acceptance of distracted drivers.

Feedback-related potentials in a gambling task with randomised reward.

Event-related potentials (ERPs) time-locked to decision outcomes are reported. Participants engaged in a gambling task (see [1] for details) in which they decided between a risky and a safe option (presented as different coloured shapes) on each trial (416 in total). Each decision was associated with (fully randomised) feedback about the reward outcome (Win/Loss) and its magnitude (varying as a function of decision response; 5-9 points for Risky decisions and 1-4 points for Safe decisions). Here, we show data demonstrating: (a) the influence of Win feedback in the preceding outcome (Outcome t-1) on activity related to the current outcome (Outcome t ); (b) difference wave analysis for outcome expectancy- separating Expected Outcomes (consecutive Loss trials subtracted from consecutive reward) from Unexpected Outcomes (subtracting Loss t-1Win t trials from Win t-1Loss t trials); (c) difference waves separating Switch and Stay responses for Outcome Expectancy; (d) the effect of magnitude induced by decisions (Risk t vs. Safe t ) on Outcome Expectancy; and finally, (e) expectations reflected by response switch direction (Risk to Safe responses vs. Safe to Risk t ) on the FRN at Outcome t .