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94; 95% CI, 0.90-0.99; p = 0.01), whereas a nonsignificant association was seen between higher strain and increased acute hospital mortality (odds ratio, 1.04; 95% CI, 1.00-1.10; p = 0.07). The relationship between periods of high ICU strain and acute hospital mortality was strongest when bed census was composed of higher acuity patients (odds ratio, 1.05; 95% CI, 1.01-1.10; p = 0.03). No relationship was seen between high strain and ICU mortality. CONCLUSIONS In closed staffing models of care, variations in bed census within individual ICUs was associated with patient's predicted risk of acute hospital mortality, particularly when its standardized bed census consisted of sicker patients.Prediction models aim to use available data to predict a health state or outcome that has not yet been observed. Prediction is primarily relevant to clinical practice, but is also used in research, and administration. While prediction modeling involves estimating the relationship between patient factors and outcomes, it is distinct from casual inference. Prediction modeling thus requires unique considerations for development, validation, and updating. This document represents an effort from editors at 31 respiratory, sleep, and critical care medicine journals to consolidate contemporary best practices and recommendations related to prediction study design, conduct, and reporting. Herein, we address issues commonly encountered in submissions to our various journals. Key topics include considerations for selecting predictor variables, operationalizing variables, dealing with missing data, the importance of appropriate validation, model performance measures and their interpretation, and good reporting practices. Supplemental discussion covers emerging topics such as model fairness, competing risks, pitfalls of "modifiable risk factors", measurement error, and risk for bias. This guidance is not meant to be overly prescriptive; we acknowledge that every study is different, and no set of rules will fit all cases. Additional best practices can be found in the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines, to which we refer readers for further details.We recently suggested new statistical metrics for routine reporting in random-effects meta-analyses to convey evidence strength for scientifically meaningful effects under effect heterogeneity. First, given a chosen threshold of meaningful effect size, we suggested reporting the estimated proportion of true effect sizes above this threshold. Second, we suggested reporting the proportion of effect sizes below a second, possibly symmetric, threshold in the opposite direction from the estimated mean. Our previous methods applied when the true effects are approximately normal, when the number of studies is relatively large, and when the proportion is between approximately 0.15 and 0.85. Here, we additionally describe robust methods for point estimation and inference that perform well under considerably more general conditions, as we validate in an extensive simulation study. The methods are implemented in the R package MetaUtility (function prop_stronger). We describe application of the robust methods to conducting sensitivity analyses for unmeasured confounding in meta-analyses.We take steps toward causally interpretable meta-analysis by describing methods for transporting causal inferences from a collection of randomized trials to a new target population, one trial at a time and pooling all trials. Apoptosis inhibitor We discuss identifiability conditions for average treatment effects in the target population and provide identification results. We show that assuming inferences are transportable from all trials in the collection to the same target population has implications for the law underlying the observed data. We propose average treatment effect estimators that rely on different working models and provide code for their implementation in statistical software. We discuss how to use the data to examine whether transported inferences are homogeneous across the collection of trials, sketch approaches for sensitivity analysis to violations of the identifiability conditions, and describe extensions to address nonadherence in the trials. Last, we illustrate the proposed methods using data from the HALT-C multicenter trial.Motivation has activational and directional components. Mesolimbic dopamine is critical for the regulation of behavioral activation and effort-related processes in motivated behaviors. Impairing mesolimbic dopamine function leads to fatigue and anergia, but leaves intact other aspects of reinforce seeking behaviors, such as the consummatory or hedonic component. In male Swiss mice, we characterized the impact of dopamine antagonism on the selection of concurrently presented stimuli that have different vigor requirements. We analyzed running wheel activity versus sucrose solution intake, typically used as a measure of anhedonia. Results are compared with data from nonconcurrent presentation to those stimuli. In the concurrent presentation experiment, control mice preferred to spend time running compared to sucrose intake. Dopamine antagonism shifted relative reinforcer preference, reducing time spent on the running wheel, but actually increasing time-consuming sucrose. Mice increased frequency of bouts for both reinforcers, suggesting that there was fatigue in the running wheel rather than aversion. Moreover, satiation or habituation by preexposing animals to both reinforcers did not shift preferences. In the nonconcurrent experiments, haloperidol reduced running wheel but had no impact on sucrose consumption. Dopamine antagonism did not change preference for sucrose or total volume consumed. Additional correlational analyses indicated that baseline differences in sucrose consumption were independent of baseline running or novelty exploration. Thus, dopamine antagonism seems to have anergic rather than anhedonic effects, and the concurrent presentation in this setting could be useful for assessing preferences based on effort requirements.OBJECTIVES We performed a study to assess the effects of a quality improvement (QI) initiative on the rates of postvariceal bleeding surveillance upper endoscopy (EGD). METHODS We identified patients with cirrhosis hospitalized with variceal bleeding and assessed the rates of timely (≤4 weeks) EGD before and after a QI initiative. RESULTS Preintervention 16% (5 of 32) of patients underwent timely surveillance EGD. We developed a standardized ordering template for gastroenterology fellows and reserved postvariceal EGD scheduling slots. Postintervention 43% (12 of 28) of patients underwent timely surveillance EGD. DISCUSSION A QI intervention was associated with a 27% absolute increase in timely surveillance EGDs.