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Cardiovascular disease (CVD) significantly contributes to morbidity and mortality after liver transplantation (LT). Cirrhotic cardiomyopathy (CCM) is a risk factor for CVD after transplant. CCM criteria were originally introduced in 2005 with a revision proposed in 2020 reflecting echocardiographic technology advancements. This study assesses the two criteria sets in predicting major adverse cardiac events (MACE) after transplant. This single-center retrospective study reviewed adult LT recipients between January 1, 2009, and December 31, 2018. Patients with insufficient pre-LT echocardiographic data, prior ischemic heart disease, portopulmonary hypertension, or longitudinal care elsewhere were excluded. The primary composite outcome was MACE (arrhythmia, heart failure, cardiac arrest, and/or cardiac death) after transplant. Of 1165 patients, 210 met the eligibility criteria. CCM was present in 162 patients (77%) per the original criteria and 64 patients (30%) per the revised criteria. There were 44 MACE and 31 deaths in the study period. Of the deaths, 38.7% occurred secondary to CVD. CCM defined by the original criteria was not associated with MACE after LT (p = 0.21), but the revised definition was significantly associated with MACE (hazard ratio [HR], 1.93; 95% confidence interval, 1.05-3.56; p = 0.04) on multivariable analysis. Echocardiographic variable analysis demonstrated low septal e as the most predictive variable for MACE after LT (HR, 3.45; p

Most data analyses rely on models. To complement statistical models, psychologists have developed cognitive models, which translate observed variables into psychologically interesting constructs. Response time models, in particular, assume that response time and accuracy are the observed expression of latent variables including 1) ease of processing, 2) response caution, 3) response bias, and 4) non-decision time. Inferences about these psychological factors, hinge upon the validity of the models' parameters. Here, we use a blinded, collaborative approach to assess the validity of such model-based inferences. Seventeen teams of researchers analyzed the same 14 data sets. In each of these two-condition data sets, we manipulated properties of participants' behavior in a two-alternative forced choice task. The contributing teams were blind to the manipulations, and had to infer what aspect of behavior was changed using their method of choice. The contributors chose to employ a variety of models, estimation methods, and inference procedures. Our results show that, although conclusions were similar across different methods, these "modeler's degrees of freedom" did affect their inferences. Interestingly, many of the simpler approaches yielded as robust and accurate inferences as the more complex methods. We recommend that, in general, cognitive models become a typical analysis tool for response time data. In particular, we argue that the simpler models and procedures are sufficient for standard experimental designs. We finish by outlining situations in which more complicated models and methods may be necessary, and discuss potential pitfalls when interpreting the output from response time models.

Object representations from convolutional neural network (CNN) models of computer vision (LeCun, Bengio, & Hinton, 2015) were used to drive a cognitive model of decision making, the linear ballistic accumulator (LBA) model (Brown & Heathcote, 2008), to predict errors and response times (RTs) in a novel object recognition task in humans. CNNs have become very successful at visual tasks like classifying objects in real-world images (e.g., He, Zhang, Ren, & Sun, 2015; Krizhevsky, Sutskever, & Hinton, 2012). We asked whether object representations learned by CNNs previously trained on a large corpus of natural images could be used to predict performance recognizing novel objects the network has never been trained on; we used novel "Greebles," "Ziggerins," and "Sheinbugs" that have been used in a number of previous object recognition studies. We specifically investigated whether a model combining high-level CNN representations of these novel objects could be used to drive an LBA model of decision making to account for errors and RTs in a same-different matching task (from Richler et al., 2019). Combining linearly transformed CNN object representations with the LBA provided reasonable accounts of performance not only on average, but at the individual-participant level and the item level as well. We frame the findings in the context of growing interest in using CNN models to understand visual object representations and the promise of using CNN representations to extend cognitive models to explain more complex aspects of human behavior.

The development of visual expertise is accompanied by enhanced visual object recognition memory within an expert domain. We aimed to understand the relationship between expertise and memory by modeling cognitive mechanisms. Participants with a measured range of birding expertise were recruited and tested on memory for birds (expert domain) and cars (novice domain). Participants performed an old-new continuous recognition memory task whereby on each trial an image of a bird or car was presented that was either new or had been presented earlier with lag "j." The Linear Ballistic Accumulator model (LBA; Brown & Heathcote, 2008) was first used to decompose accuracy and response time (RT) into drift rate, response threshold, and nondecision time, with the measured level of visual expertise as a potential covariate on each model parameter. An Expertise × Category interaction was observed on drift rates such that expertise was positively correlated with memory performance recognizing bird images but not car images as old versus new. To then model the underlying processes responsible for variation in drift rate with expertise, we used a model of drift rates building on the Exemplar-Based Random Walk model (Nosofsky, Cox, Cao, & Shiffrin, 2014; Nosofsky & Palmeri, 1997), which revealed that expertise was associated with increases in memory strength and increases in the distinctiveness of stored exemplars. Taken together, we provide insight using formal cognitive modeling into how improvements in recognition memory with expertise are driven by enhancements in the representations of objects in an expert domain.

Recognition memory accuracy is harmed by prior testing (a.k.a., output interference [OI]; Tulving & Arbuckle, 1966). In several experiments, we interpolated various tasks between recognition test trials. The stimuli and the tasks were more similar (lexical decision [LD] of words and nonwords) or less similar (gender identification of male and female faces) to the stimuli and task used in recognition testing. Not only did the similarity between the interpolated and recognition tasks not affect recognition accuracy but performance of the interpolated task caused no interference in subsequent recognition testing. Only the addition of recognition trials caused OI. When we presented a block of LD trials or gender identification trials before the recognition test, a decrease in accuracy was observed in the subsequent recognition tests. These results suggest a distinction between temporal context and task context, such that recognition memory performance is determined by the salience of the context cues, and the use of temporal context cues is associated with OI.

Many models of recognition are derived from models originally applied to perception tasks, which assume that decisions from trial to trial are independent. While the independence assumption is violated for many perception tasks, we present the results of several experiments intended to relate memory and perception by exploring sequential dependencies in recognition. The findings from these experiments disconfirm the independence assumption for recognition memory. In addition, the pattern of sequential dependencies observed in recognition differs from that observed for many perception tasks. This suggests that sequential dependencies arise from mnemonic or perceptual processes and not from decision processes that should be common to memory and perception tasks. (Contains 17 figures and 2 footnotes.)

Context: Prior studies of the relationship between physical activity and incident type 2 diabetes mellitus (T2DM) relied primarily on questionnaires at a single time point. Objective: We sought to investigate the relationship between physical activity and incident T2DM with an innovative approach using data from commercial wearable devices linked to electronic health records in a real-world population. Methods: Using All of Us participants’ accelerometer data from their personal Fitbit devices, we used a time-varying Cox proportional hazards models with repeated measures of physical activity for the outcome of incident T2DM. We evaluated for effect modification with age, sex, body mass index (BMI), and sedentary time using multiplicative interaction terms. Results: From 5677 participants in the All of Us Research Program (median age 51 years; 74% female; 89% White), there were 97 (2%) cases of incident T2DM over a median follow-up period of 3.8 years between 2010 to 2021. In models adjusted for age, sex, and race, the hazard of incident diabetes was reduced by 44% (95% CI, 15%-63%; P=0.01) when comparing those with an average daily step count of 10 700 to those with 6000. Similar benefits were seen comparing groups based on average duration of various intensities of activity (eg, lightly active, fairly active, very active). There was no evidence for effect modification by age, sex, BMI, or sedentary time. Conclusion: Greater time in any type of physical activity intensity was associated with lower risk of T2DM irrespective of age, sex, BMI, or sedentary time. [ABSTRACT FROM AUTHOR]

Pulmonary hypertension is a common sequelae of left heart failure and may present as isolated postcapillary pulmonary hypertension (Ipc‐PH) or combined pre‐ and postcapillary pulmonary hypertension (Cpc‐PH). Clinical features associated with progression from Ipc‐PH to Cpc‐PH have not yet been described. We extracted clinical data from patients who underwent right heart catheterizations (RHC) on two separate occasions. Ipc‐PH was defined as mean pulmonary pressure >20 mmHg, pulmonary capillary wedge pressure >15 mmHg, and pulmonary vascular resistance (PVR) < 3 WU. Progression to Cpc‐PH required an increase in PVR to ≥3 WU. We performed a retrospective cohort study with repeated assessments comparing subjects that progressed to Cpc‐PH to subjects that remained with Ipc‐PH. Of 153 patients with Ipc‐PH at baseline who underwent a repeat RHC after a median of 0.7 years (IQR 0.2, 2.1), 33% (50/153) had developed Cpc‐PH. In univariate analysis comparing the two groups at baseline, body mass index (BMI) and right atrial pressure were lower, while the prevalence of moderate or worse mitral regurgitation (MR) was higher among those who progressed. In age‐ and sex‐adjusted multivariable analysis, only BMI (OR 0.94, 95% CI 0.90–0.99, p = 0.017, C = 0.655) and moderate or worse MR (OR 3.00, 95% CI 1.37–6.60, p = 0.006, C = 0.654) predicted progression, but with poor discriminatory power. This study suggests that clinical features alone cannot distinguish patients at risk for development of Cpc‐PH and support the need for molecular and genetic studies to identify biomarkers of progression. [ABSTRACT FROM AUTHOR]