Evaluating the perceptions of workplace-based assessments in surgical training: a systematic review

INTRODUCTION

Workplace-based assessments (WBAs) are intended to maximise learning opportunities in surgical training. There is speculation as to whether mandatory assessments in this form contribute to a tick-box culture. The objective of this review was to investigate surgical trainees' attitudes towards WBAs.

METHODS

This systematic review of qualitative studies was performed in accordance with the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) statement. The literature was searched on the Medline®, Embase™, PubMed and Web of Science™ databases on 22 March 2022.

RESULTS

Sixteen studies were included in the review, mostly carried out on users of the Intercollegiate Surgical Curriculum Programme portfolio in the UK. Trainees felt that WBAs were educationally useful, providing opportunity for feedback, but this was overshadowed by a pressure to reach a set annual quota for WBAs and achieve high scores. Other themes included inaccurate recording of WBAs, the role of WBAs as formative or summative assessments, engagement and accessibility of trainers, and lack of time to complete WBAs.

CONCLUSIONS

Negative perceptions about WBAs were widespread among surgical trainees despite a recognition of their capacity to facilitate learning. This review supports the recent removal of the annual quota for WBAs in UK surgical training programmes.

A systematic review of the effectiveness of community-based interventions aimed at improving health literacy of parents/carers of children

AIM

The aim of this systematic review was to examine the effectiveness of community-based health literacy interventions in improving the health literacy of parents.

METHODS

A systematic review of six databases - MEDLINE, PsycINFO, CINAHL, Cochrane Library, Embase, and Education Source - was conducted to identify relevant articles. Risk of bias was assessed using version two of the Cochrane risk of bias tool for randomised controlled trials or the Cochrane collaboration risk of bias in non-randomised studies of interventions. The study findings were grouped and synthesised following the synthesis without meta-analysis framework.

RESULTS

Eleven community-based health literacy interventions for parents were identified. Study design included randomised controlled trials (n = 4), non-randomised studies with comparison group (n = 4), and non-randomised studies without a comparison group (n = 3). Interventions were delivered digitally, in person or a combination of the two. The risk of bias was high in over half the studies (n = 7). The main findings of the studies showed some potential for both in person and digital interventions to increase parental health literacy. Studies were heterogeneous preventing a meta-analysis.

CONCLUSION

Community-based, health literacy interventions have been identified as potential methods for enhancing parental health literacy. Due to the small number of included studies and their potential for bias, these results must be interpreted with caution. This study emphasises the need for additional theory and evidence-based research on the long-term effects of community interventions.

A Retrospective Service- Evaluation of Implant Success, Survival, Periimplant Health and Prosthetic Complications in a Cohort of Head and Neck Cancer Patients

OBJECTIVES

To determine the success, survival, peri-implant health and prosthetic complications in head and neck cancer patients receiving oral rehabilitation utilising dental implants between 2008 and the present day.

MATERIALS AND METHODS

Service evaluation. Survival Group: Retrospective review of records to determine implant survival and prosthetic complications. Success Group: Examination to determine implant success and health.

RESULTS

Survival Group: 260 implants in 81 individuals, median follow up 49.2 months. 89.3% implant survival at 96 months, no further failures up to 133 months. 40.9% individuals required repair or remake of prosthesis by 72 months - mostly denture re-lines. Success group: 164 implants in 48 individuals, median follow up 56 months. Peri-implant mucositis detected in 22% of fixtures (37.5% individuals); peri-implantitis in 12.8% (25% individuals); 33.3% fixtures exhibiting periimplantitis at 120 months. Previous smoking significantly associated with development of peri-implantitis (HR 2.372, p=0.032, 95CI:1.232, 93.317). Compromised survival (e.g. peri-implantitis), absolute (not in mouth) or clinical failure estimated to occur in 28.1% fixtures at 101 months, mostly due to peri-implantitis.

CONCLUSIONS

There is a large burden of ongoing care in this cohort, requiring interventions to improve peri-implant health and maintain complex prostheses. Oral rehabilitation and ongoing maintenance in this cohort is complex and multi-disciplinary.

Energy Guided Diffusion for Generating Neurally Exciting Images

In recent years, most exciting inputs (MEIs) synthesized from encoding models of neuronal activity have become an established method to study tuning properties of biological and artificial visual systems. However, as we move up the visual hierarchy, the complexity of neuronal computations increases. Consequently, it becomes more challenging to model neuronal activity, requiring more complex models. In this study, we introduce a new attention readout for a convolutional data-driven core for neurons in macaque V4 that outperforms the state-of-the-art task-driven ResNet model in predicting neuronal responses. However, as the predictive network becomes deeper and more complex, synthesizing MEIs via straightforward gradient ascent (GA) can struggle to produce qualitatively good results and overfit to idiosyncrasies of a more complex model, potentially decreasing the MEI's model-to-brain transferability. To solve this problem, we propose a diffusion-based method for generating MEIs via Energy Guidance (EGG). We show that for models of macaque V4, EGG generates single neuron MEIs that generalize better across architectures than the state-of-the-art GA while preserving the within-architectures activation and requiring 4.7x less compute time. Furthermore, EGG diffusion can be used to generate other neurally exciting images, like most exciting natural images that are on par with a selection of highly activating natural images, or image reconstructions that generalize better across architectures. Finally, EGG is simple to implement, requires no retraining of the diffusion model, and can easily be generalized to provide other characterizations of the visual system, such as invariances. Thus EGG provides a general and flexible framework to study coding properties of the visual system in the context of natural images.

Use of aequorin-based indicators for monitoring Ca2+ in acidic organelles

Over the last years, there is accumulating evidence that acidic organelles can accumulate and release Ca²⁺ upon cell activation. Hence, reliable recording of Ca²⁺ dynamics in these compartments is essential for understanding the physiopathological aspects of acidic organelles. Genetically encoded Ca²⁺ indicators (GECIs) are valuable tools to monitor Ca²⁺ in specific locations, although their use in acidic compartments is challenging due to the pH sensitivity of most available fluorescent GECIs. By contrast, bioluminescent GECIs have a combination of features (marginal pH sensitivity, low background, no phototoxicity, no photobleaching, high dynamic range and tunable affinity) that render them advantageous to achieve an enhanced signal-to-noise ratio in acidic compartments. This article reviews the use of bioluminescent aequorin-based GECIs targeted to acidic compartments. A need for more measurements in highly acidic compartments is identified.

Towards a Foundation Model of the Mouse Visual Cortex

Understanding the brain's perception algorithm is a highly intricate problem, as the inherent complexity of sensory inputs and the brain's nonlinear processing make characterizing sensory representations difficult. Recent studies have shown that functional models-capable of predicting large-scale neuronal activity in response to arbitrary sensory input-can be powerful tools for characterizing neuronal representations by enabling high-throughput in silico experiments. However, accurately modeling responses to dynamic and ecologically relevant inputs like videos remains challenging, particularly when generalizing to new stimulus domains outside the training distribution. Inspired by recent breakthroughs in artificial intelligence, where foundation models-trained on vast quantities of data-have demonstrated remarkable capabilities and generalization, we developed a "foundation model" of the mouse visual cortex: a deep neural network trained on large amounts of neuronal responses to ecological videos from multiple visual cortical areas and mice. The model accurately predicted neuronal responses not only to natural videos but also to various new stimulus domains, such as coherent moving dots and noise patterns, underscoring its generalization abilities. The foundation model could also be adapted to new mice with minimal natural movie training data. We applied the foundation model to the MICrONS dataset: a study of the brain that integrates structure with function at unprecedented scale, containing nanometer-scale morphology, connectivity with >500,000,000 synapses, and function of >70,000 neurons within a ~1mm3 volume spanning multiple areas of the mouse visual cortex. This accurate functional model of the MICrONS data opens the possibility for a systematic characterization of the relationship between circuit structure and function. By precisely capturing the response properties of the visual cortex and generalizing to new stimulus domains and mice, foundation models can pave the way for a deeper understanding of visual computation.