SAEM systematic online academic resource (SOAR) review: Gastrointestinal illnesses.

Background and Objectives: Free open access medical education (FOAM) has become an essential tool for emergency medicine (EM) education and can be valuable to clinicians as a point-of-care resource. The development of the revised Medical Education Translational Resources Impact and Quality (rMETRIQ) tool provides a standardized means of quality assessment. Previous entries of the Society for Academic Emergency Medicine systematic online academic resource (SOAR) series have focused on renal, endocrine, and sickle cell disorders. In this iteration, we strive to identify, curate, and describe FOAM topics specific to acute gastrointestinal (GI) illnesses. Methods: We searched 389 keywords across 11 GI topics that were modified from the 2019 Model of the Clinical Practice of EM (EM Model) using the search engine Google FOAM and within the top 50 websites listed on Academic Life in Emergency Medicine's Social Media Index. The sites underwent preliminary screening to eliminate resources that were not relevant to EM or GI illnesses. Identified resources were evaluated with the rMETRIQ tool by five board-certified EM physicians who received rMETRIQ tool rater training. Results: After duplicates of the initial 39,505 resources were eliminated, 8059 remained. Primary screening resulted in a final 1202 resources. The most common categories were large bowel (18%), small bowel (13%), stomach (11%), esophagus (11%), biliary (11%), and liver (10%). Many resources covered multiple topics and subtopics. The final mean intraclass correlation coefficient among the five physicians was 0.95 (95% CI 0.92-0.98) for rMETRIQ scoring. We identified 256 sites considered "high quality" with a rMETRIQ score of 16 or higher as designated in prior reviews. Conclusions: This iteration of the SOAR review resulted in the highest number of high-quality resources compared to other SOAR reviews, with 21% of resources thus far scoring ≥ 16. A final list of high-quality resources can guide trainees, educator recommendations, and FOAM authors.

Emergency and urgent care capacity in a resource-limited setting: an assessment of health facilities in western Kenya.

OBJECTIVE: Injuries, trauma and non-communicable diseases are responsible for a rising proportion of death and disability in low-income and middle-income countries. Delivering effective emergency and urgent healthcare for these and other conditions in resource-limited settings is challenging. In this study, we sought to examine and characterise emergency and urgent care capacity in a resource-limited setting. METHODS: We conducted an assessment within all 30 primary and secondary hospitals and within a stratified random sampling of 30 dispensaries and health centres in western Kenya. The key informants were the most senior facility healthcare provider and manager available. Emergency physician researchers utilised a semistructured assessment tool, and data were analysed using descriptive statistics and thematic coding. RESULTS: No lower level facilities and 30% of higher level facilities reported having a defined, organised approach to trauma. 43% of higher level facilities had access to an anaesthetist. The majority of lower level facilities had suture and wound care supplies and gloves but typically lacked other basic trauma supplies. For cardiac care, 50% of higher level facilities had morphine, but a minority had functioning ECG, sublingual nitroglycerine or a defibrillator. Only 20% of lower level facilities had glucometers, and only 33% of higher level facilities could care for diabetic emergencies. No facilities had sepsis clinical guidelines. CONCLUSIONS: Large gaps in essential emergency care capabilities were identified at all facility levels in western Kenya. There are great opportunities for a universally deployed basic emergency care package, an advanced emergency care package and facility designation scheme, and a reliable prehospital care transportation and communications system in resource-limited settings.

Automatic independent component labeling for artifact removal in fMRI.

Blood oxygenation level dependent (BOLD) signals in functional magnetic resonance imaging (fMRI) are often small compared to the level of noise in the data. The sources of noise are numerous including different kinds of motion artifacts and physiological noise with complex patterns. This complicates the statistical analysis of the fMRI data. In this study, we propose an automatic method to reduce fMRI artifacts based on independent component analysis (ICA). We trained a supervised classifier to distinguish between independent components relating to a potentially task-related signal and independent components clearly relating to structured noise. After the components had been classified as either signal or noise, a denoised fMR time-series was reconstructed based only on the independent components classified as potentially task-related. The classifier was a novel global (fixed structure) decision tree trained in a Neyman-Pearson (NP) framework, which allowed the shape of the decision regions to be controlled effectively. Additionally, the conservativeness of the classifier could be tuned by modifying the NP threshold. The classifier was tested against the component classifications by an expert with the data from a category learning task. The test set as well as the expert were different from the data used for classifier training and the expert labeling the training set. The misclassification rate was between 0.2 and 0.3 for both the event-related and blocked designs and it was consistent among variety of different NP thresholds. The effects of denoising on the group-level statistical analyses were as expected: The denoising generally decreased Z-scores in the white matter, where extreme Z-values can be expected to reflect artifacts. A similar but weaker decrease in Z-scores was observed in the gray matter on average. These two observations suggest that denoising was likely to reduce artifacts from gray matter and could be useful to improve the detection of activations. We conclude that automatic ICA-based denoising offers a potentially useful approach to improve the quality of fMRI data and consequently increase the accuracy of the statistical analysis of these data.

Putting feelings into words: affect labeling disrupts amygdala activity in response to affective stimuli.

Putting feelings into words (affect labeling) has long been thought to help manage negative emotional experiences; however, the mechanisms by which affect labeling produces this benefit remain largely unknown. Recent neuroimaging studies suggest a possible neurocognitive pathway for this process, but methodological limitations of previous studies have prevented strong inferences from being drawn. A functional magnetic resonance imaging study of affect labeling was conducted to remedy these limitations. The results indicated that affect labeling, relative to other forms of encoding, diminished the response of the amygdala and other limbic regions to negative emotional images. Additionally, affect labeling produced increased activity in a single brain region, right ventrolateral prefrontal cortex (RVLPFC). Finally, RVLPFC and amygdala activity during affect labeling were inversely correlated, a relationship that was mediated by activity in medial prefrontal cortex (MPFC). These results suggest that affect labeling may diminish emotional reactivity along a pathway from RVLPFC to MPFC to the amygdala.

The neural basis of loss aversion in decision-making under risk.

People typically exhibit greater sensitivity to losses than to equivalent gains when making decisions. We investigated neural correlates of loss aversion while individuals decided whether to accept or reject gambles that offered a 50/50 chance of gaining or losing money. A broad set of areas (including midbrain dopaminergic regions and their targets) showed increasing activity as potential gains increased. Potential losses were represented by decreasing activity in several of these same gain-sensitive areas. Finally, individual differences in behavioral loss aversion were predicted by a measure of neural loss aversion in several regions, including the ventral striatum and prefrontal cortex.

The neural correlates of motor skill automaticity.

Acquisition of a new skill is generally associated with a decrease in the need for effortful control over performance, leading to the development of automaticity. Automaticity by definition has been achieved when performance of a primary task is minimally affected by other ongoing tasks. The neural basis of automaticity was examined by testing subjects in a serial reaction time (SRT) task under both single-task and dual-task conditions. The diminishing cost of dual-task performance was used as an index for automaticity. Subjects performed the SRT task during two functional magnetic imaging sessions separated by 3 h of behavioral training over multiple days. Behavioral data showed that, by the end of testing, subjects had automated performance of the SRT task. Before behavioral training, performance of the SRT task concurrently with the secondary task elicited activation in a wide network of frontal and striatal regions, as well as parietal lobe. After extensive behavioral training, dual-task performance showed comparatively less activity in bilateral ventral premotor regions, right middle frontal gyrus, and right caudate body; activity in other prefrontal and striatal regions decreased equally for single-task and dual-task conditions. These data suggest that lateral and dorsolateral prefrontal regions, and their corresponding striatal targets, subserve the executive processes involved in novice dual-task performance. The results also showed that supplementary motor area and putamen/globus pallidus regions showed training-related decreases for sequence conditions but not for random conditions, confirming the role of these regions in the representation of learned motor sequences.

Dissociable correlates of recollection and familiarity within the medial temporal lobes.

Regions in the medial temporal lobes (MTL) have long been implicated in the formation of new memories for events, however, it is unclear whether different MTL subregions support different memory processes. Here, we used event-related functional magnetic resonance imaging (fMRI) to examine the degree to which two recognition memory processes-recollection and familiarity-were supported by different MTL subregions. Results showed that encoding activity in the rhinal cortex selectively predicted familiarity-based recognition, whereas, activity in the hippocampus and posterior parahippocampal cortex selectively predicted recollection. Collectively, these results support the view that different subregions within the MTL memory system implement unique encoding processes that differentially support familiarity and recollection.