Does video feedback analysis improve CPR performance in phase 5 medical students?

BACKGROUND: The use of simulation in medical education is increasing, with students taught and assessed using simulated patients and manikins. Medical students at Queen's University of Belfast are taught advanced life support cardiopulmonary resuscitation as part of the undergraduate curriculum. Teaching and feedback in these skills have been developed in Queen's University with high-fidelity manikins. This study aimed to evaluate the effectiveness of video compared to verbal feedback in assessment of student cardiopulmonary resuscitation performance. METHODS: Final year students participated in this study using a high-fidelity manikin, in the Clinical Skills Centre, Queen's University Belfast. Cohort A received verbal feedback only on their performance and cohort B received video feedback only. Video analysis using 'StudioCode' software was distributed to students. Each group returned for a second scenario and evaluation 4 weeks later. An assessment tool was created for performance assessment, which included individual skill and global score evaluation. RESULTS: One hundred thirty eight final year medical students completed the study. 62 % were female and the mean age was 23.9 years. Students having video feedback had significantly greater improvement in overall scores compared to those receiving verbal feedback (p = 0.006, 95 % CI: 2.8-15.8). Individual skills, including ventilation quality and global score were significantly better with video feedback (p = 0.002 and p < 0.001, respectively) when compared with cohort A. There was a positive change in overall score for cohort B from session one to session two (p < 0.001, 95 % CI: 6.3-15.8) indicating video feedback significantly benefited skill retention. In addition, using video feedback showed a significant improvement in the global score (p < 0.001, 95 % CI: 3.3-7.2) and drug administration timing (p = 0.004, 95 % CI: 0.7-3.8) of cohort B participants, from session one to session two. CONCLUSIONS: There is increased use of simulation in medicine but a paucity of published data comparing feedback methods in cardiopulmonary resuscitation training. Our study shows the use of video feedback when teaching cardiopulmonary resuscitation is more effective than verbal feedback, and enhances skill retention. This is one of the first studies to demonstrate the benefit of video feedback in cardiopulmonary resuscitation teaching.

Nonclinical safety assessment of recombinant human acid sphingomyelinase (rhASM) for the treatment of acid sphingomyelinase deficiency:the utility of animal models of disease in the toxicological evaluation of potential therapeutics.

Recombinant human acid sphingomyelinase (rhASM) is being developed as an enzyme replacement therapy for patients with acid sphingomyelinase deficiency (Niemann-Pick disease types A and B), which causes sphingomyelin to accumulate in lysosomes. In the acid sphingomyelinase knock-out (ASMKO) mouse, intravenously administered rhASM reduced tissue sphingomyelin levels in a dose-dependent manner. When rhASM was administered to normal rats, mice, and dogs, no toxicity was observed up to a dose of 30mg/kg. However, high doses of rhASM≥10mg/kg administered to ASMKO mice resulted in unexpected toxicity characterized by cardiovascular shock, hepatic inflammation, adrenal hemorrhage, elevations in ceramide and cytokines (especially IL-6, G-CSF, and keratinocyte chemoattractant [KC]), and death. The toxicity could be completely prevented by the administration of several low doses (3mg/kg) of rhASM prior to single or repeated high doses (≥20mg/kg). These results suggest that the observed toxicity involves the rapid breakdown of large amounts of sphingomyelin into ceramide and/or other toxic downstream metabolites, which are known signaling molecules with cardiovascular and pro-inflammatory effects. Our results suggest that the nonclinical safety assessment of novel therapeutics should include the use of specific animal models of disease whenever feasible.

Superconductivity on the brink of spin-charge order in a doped honeycomb bilayer.

Using a controlled weak-coupling renormalization group approach, we establish the mechanism of unconventional superconductivity in the vicinity of spin or charge ordered excitonic states for the case of electrons on the Bernal stacked bilayer honeycomb lattice. With one electron per site, this system, physically realized in bilayer graphene, is unstable towards a spontaneous symmetry breaking. Repulsive interactions favor excitonic order, such as a charge nematic and/or a layer antiferromagnet. We find that upon adding charge carriers to the system, the excitonic order is suppressed, and unconventional superconductivity appears in its place, before it is replaced by a Fermi liquid. We focus on firmly establishing this phenomenon using the renormalization group formalism within an idealized model with parabolic touching of conduction and valence bands.

Passive exposure to task-relevant stimuli enhances categorization learning.

Learning to perform a perceptual decision task is generally achieved through sessions of effortful practice with feedback. Here, we investigated how passive exposure to task-relevant stimuli, which is relatively effortless and does not require feedback, influences active learning. First, we trained mice in a sound-categorization task with various schedules combining passive exposure and active training. Mice that received passive exposure exhibited faster learning, regardless of whether this exposure occurred entirely before active training or was interleaved between active sessions. We next trained neural-network models with different architectures and learning rules to perform the task. Networks that use the statistical properties of stimuli to enhance separability of the data via unsupervised learning during passive exposure provided the best account of the behavioral observations. We further found that, during interleaved schedules, there is an increased alignment between weight updates from passive exposure and active training, such that a few interleaved sessions can be as effective as schedules with long periods of passive exposure before active training, consistent with our behavioral observations. These results provide key insights for the design of efficient training schedules that combine active learning and passive exposure in both natural and artificial systems.

Exploration biases forelimb reaching strategies.

The brain can generate actions, such as reaching to a target, using different movement strategies. We investigate how such strategies are learned in a task where perched head-fixed mice learn to reach to an invisible target area from a set start position using a joystick. This can be achieved by learning to move in a specific direction or to a specific endpoint location. As mice learn to reach the target, they refine their variable joystick trajectories into controlled reaches, which depend on the sensorimotor cortex. We show that individual mice learned strategies biased to either direction- or endpoint-based movements. This endpoint/direction bias correlates with spatial directional variability with which the workspace was explored during training. Model-free reinforcement learning agents can generate both strategies with similar correlation between variability during training and learning bias. These results provide evidence that reinforcement of individual exploratory behavior during training biases the reaching strategies that mice learn.

Passive exposure to task-relevant stimuli enhances categorization learning.

Learning to perform a perceptual decision task is generally achieved through sessions of effortful practice with feedback. Here, we investigated how passive exposure to task-relevant stimuli, which is relatively effortless and does not require feedback, influences active learning. First, we trained mice in a sound-categorization task with various schedules combining passive exposure and active training. Mice that received passive exposure exhibited faster learning, regardless of whether this exposure occurred entirely before active training or was interleaved between active sessions. We next trained neural-network models with different architectures and learning rules to perform the task. Networks that use the statistical properties of stimuli to enhance separability of the data via unsupervised learning during passive exposure provided the best account of the behavioral observations. We further found that, during interleaved schedules, there is an increased alignment between weight updates from passive exposure and active training, such that a few interleaved sessions can be as effective as schedules with long periods of passive exposure before active training, consistent with our behavioral observations. These results provide key insights for the design of efficient training schedules that combine active learning and passive exposure in both natural and artificial systems.

Distinguishing Learning Rules with Brain Machine Interfaces.

Despite extensive theoretical work on biologically plausible learning rules, clear evidence about whether and how such rules are implemented in the brain has been difficult to obtain. We consider biologically plausible supervised- and reinforcement-learning rules and ask whether changes in network activity during learning can be used to determine which learning rule is being used. Supervised learning requires a credit-assignment model estimating the mapping from neural activity to behavior, and, in a biological organism, this model will inevitably be an imperfect approximation of the ideal mapping, leading to a bias in the direction of the weight updates relative to the true gradient. Reinforcement learning, on the other hand, requires no credit-assignment model and tends to make weight updates following the true gradient direction. We derive a metric to distinguish between learning rules by observing changes in the network activity during learning, given that the mapping from brain to behavior is known by the experimenter. Because brain-machine interface (BMI) experiments allow for precise knowledge of this mapping, we model a cursor-control BMI task using recurrent neural networks, showing that learning rules can be distinguished in simulated experiments using only observations that a neuroscience experimenter would plausibly have access to.

Their OSCE, not your Oscar: Simulated Patients' Perspectives.

INTRODUCTION: Simulated patients (SPs) are involved widely in the support of health care education for communication and consultation skills teaching. This study aimed to explore SPs' perspectives of their role and contribution to health professions education. METHODS: A qualitative approach was used. Twenty-two SPs (actor and non-actor) involved in teaching at the Royal College of Surgeons Ireland (RCSI) participated in one of four focus groups, which were audiorecorded. Two focus groups involved professional actors (n = 7), and two focus groups involved non-actor SPs (n = 15). Transcribed data were analysed using thematic analysis. RESULTS: The first theme 'Reward of Contribution' highlights how the SP role is situated in a position of mutual benefit for both SP and student. The second theme 'Multiple Personas' demonstrates the complexity of SP role portrayal. The third theme 'Culture and Communication' is a new finding and demonstrates the role SPs feel they have in helping to change the culture of health care practice. The fourth theme 'A touchy subject' addresses the preferences of SPs concerning involvement in both intimate and nonintimate physical examination. CONCLUSIONS: SPs believe they have an important role to play in health care education and they also learn from their involvement. SPs' perspectives need to be considered to ensure they feel supported in their role and that their unique insights remain beneficial for student learning.

Large D-2 theory of superconducting fluctuations in a magnetic field and its application to iron pnictides.

A Ginzburg-Landau approach to fluctuations of a layered superconductor in a magnetic field is used to show that the interlayer coupling can be incorporated within an interacting self-consistent theory of a single layer, in the limit of a large number of neighboring layers. The theory exhibits two phase transitions-a vortex liquid-to-solid transition is followed by a Bose-Einstein condensation into the Abrikosov lattice-illustrating the essential role of interlayer coupling. By using this theory, explicit expressions for magnetization, specific heat, and fluctuation conductivity are derived. We compare our results with recent experimental data on the iron-pnictide superconductors.

Remembrance of things practiced with fast and slow learning in cortical and subcortical pathways.

The learning of motor skills unfolds over multiple timescales, with rapid initial gains in performance followed by a longer period in which the behavior becomes more refined, habitual, and automatized. While recent lesion and inactivation experiments have provided hints about how various brain areas might contribute to such learning, their precise roles and the neural mechanisms underlying them are not well understood. In this work, we propose neural- and circuit-level mechanisms by which motor cortex, thalamus, and striatum support motor learning. In this model, the combination of fast cortical learning and slow subcortical learning gives rise to a covert learning process through which control of behavior is gradually transferred from cortical to subcortical circuits, while protecting learned behaviors that are practiced repeatedly against overwriting by future learning. Together, these results point to a new computational role for thalamus in motor learning and, more broadly, provide a framework for understanding the neural basis of habit formation and the automatization of behavior through practice.

Local online learning in recurrent networks with random feedback.

Recurrent neural networks (RNNs) enable the production and processing of time-dependent signals such as those involved in movement or working memory. Classic gradient-based algorithms for training RNNs have been available for decades, but are inconsistent with biological features of the brain, such as causality and locality. We derive an approximation to gradient-based learning that comports with these constraints by requiring synaptic weight updates to depend only on local information about pre- and postsynaptic activities, in addition to a random feedback projection of the RNN output error. In addition to providing mathematical arguments for the effectiveness of the new learning rule, we show through simulations that it can be used to train an RNN to perform a variety of tasks. Finally, to overcome the difficulty of training over very large numbers of timesteps, we propose an augmented circuit architecture that allows the RNN to concatenate short-duration patterns into longer sequences.

Learning multiple variable-speed sequences in striatum via cortical tutoring.

Sparse, sequential patterns of neural activity have been observed in numerous brain areas during timekeeping and motor sequence tasks. Inspired by such observations, we construct a model of the striatum, an all-inhibitory circuit where sequential activity patterns are prominent, addressing the following key challenges: (i) obtaining control over temporal rescaling of the sequence speed, with the ability to generalize to new speeds; (ii) facilitating flexible expression of distinct sequences via selective activation, concatenation, and recycling of specific subsequences; and (iii) enabling the biologically plausible learning of sequences, consistent with the decoupling of learning and execution suggested by lesion studies showing that cortical circuits are necessary for learning, but that subcortical circuits are sufficient to drive learned behaviors. The same mechanisms that we describe can also be applied to circuits with both excitatory and inhibitory populations, and hence may underlie general features of sequential neural activity pattern generation in the brain.

Use of xenon as a sedative for patients receiving critical care.

OBJECTIVE: Many sedative regimens are used in the intensive care setting, but none are wholly without adverse effect. Xenon is a noble gas with sedative and analgesic properties. It has been used successfully as a general anesthetic and has many desirable properties, not least of which is a minimal effect on the myocardium. In theory, xenon may provide sedation without adverse effect for certain groups of critically ill patients. The objective of this study was to assess the feasibility of using xenon as an intensive care sedative. DESIGN: Double-blind, randomized study. SETTING: Tertiary-level intensive care unit. SUBJECTS: Twenty-one patients admitted to an intensive care unit following elective thoracic surgery. INTERVENTIONS: A standard intensive care sedation regimen (intravenous propofol at 0-5 mg.kg-1.hr-1 and alfentanil 30 microg.kg-1.hr-1) was compared with a xenon sedation regimen delivered using a novel bellows-in-bottle delivery system. MEASUREMENTS AND MAIN RESULTS Each sedative regimen was continued for 8 hrs. The hemodynamic effects, additional analgesic requirements, recovery from sedation, and effect on hematological and biochemical variables were compared for the two sedation regimens. All patients were successfully sedated during the xenon regimen. The mean +/- SD end-tidal xenon concentration required to provide sedation throughout the duration of the study was 28 +/- 9.0% (range, 9-62%). Arterial systolic, diastolic, and mean pressures showed a greater tendency for negative gradients in patients receiving the propofol regimen (p <.05, p <.1, and p <.01, respectively). Recovery following xenon was significantly faster than from the standard sedation regimen (p <.0001). Hematological and biochemical laboratory markers were within normal clinical limits in both groups. CONCLUSIONS: Xenon provided satisfactory sedation in our group of patients. It was well tolerated with minimal hemodynamic effect. Recovery from this agent is extremely rapid. We have demonstrated the feasibility of using xenon within the critical care setting, without adverse effect.