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Online Courses Provide Robust Learning Gains and Improve Learner Confidence in the Foundational Biomedical Sciences
The early stages of medical school involve education in a number of foundational biomedical sciences including genetics, immunology, and physiology. However, students entering medical school may have widely varying levels of background in these areas due to differences in the availability and quality of prior education on these topics. Even students who have recently taken formal courses in these subjects may not feel confident in their level of preparation, leading to anxiety for early-stage medical students. These differences can make it difficult for instructors to create meaningful learning experiences that are appropriate for all students. Additionally, actual or perceived differences in preparation may lead fewer students from diverse backgrounds to apply to medical school. Therefore, creating an efficient and scalable way to increase students’ knowledge and confidence in these topics addresses an important need for many medical schools. We recorded pre- and post-course quiz scores for 9790 individuals who completed HMX online courses, developed in accordance with evidence-based learning practices and covering the fundamentals of biochemistry, genetics, immunology, pharmacology, and physiology. Each question was accompanied by a Likert scale question to assess the learner’s confidence in their answer. Learners’ median post-course quiz performance and self-assessed confidence significantly increased relative to pre-course quiz performance for each course. Improvements were consistent across US-based medical schools, non-US medical schools, and course runs open to the public. This indicates that online courses created using evidence-based learning practices can lead to significant increases in knowledge and confidence for many learners, helping prepare them for further medical education.
Prognosis and Risk Stratification in Dilated Cardiomyopathy With LVEF≤35%: Cardiac MRI Insights for Better Outcomes.
BACKGROUND: Current guidelines recommend implantable cardioverter defibrillators for the primary prevention of sudden cardiac death (SCD) in patients with dilated cardiomyopathy with left ventricular ejection fraction (LVEF)≤35%. However, its effectiveness is hindered by the inability to reliably discriminate between the risk of SCD and competing death of heart failure deterioration, thereby limiting its clinical utility. We aimed to refine the SCD risk stratification model based on cardiac magnetic resonance imaging for patients with dilated cardiomyopathy with LVEF≤35%. METHODS: A total of 1272 patients with dilated cardiomyopathy with LVEF≤35% who underwent cardiac magnetic resonance imaging were consecutively enrolled in this study. The primary end point is a composite of SCD or aborted SCD and the second end point is a composite of heart failure death and heart transplantation. RESULTS: Over a median follow-up of 86.3 months, 101 patients reached the primary end point. In the adjusted analysis, age (hazard ratio [HR], 1.02 [95% CI, 1.01-1.04]; P=0.006) years, a family history of SCD (HR, 2.00 [95% CI, 1.01-3.98]; P=0.05), NT-proBNP (N-terminal pro-B-type natriuretic peptide) (HR, 2.02 [95% CI, 1.18-3.44]; P=0.01), LVEF (per 5% HR, 0.79 [95% CI, 0.66-0.95]; P=0.01), and late gadolinium enhancement≥7.5% (HR, 4.11[95% CI, 2.72-6.21]; P<0.001) were associated with SCD or aborted SCD. Left atrial volume index≥68.3 mL/m2 was an independent predictor of the secondary end point (adjusted HR, 1.65 [95% CI, 1.13-2.40]; P=0.009). Compared with late gadolinium enhancement<7.5%, patients with late gadolinium enhancement≥7.5% and LVEF≤20% had a 7.12-fold higher risk of experiencing SCD events in competing Cox analysis (annual event rate, 4.8%). CONCLUSIONS: Patients with dilated cardiomyopathy with late gadolinium enhancement≥7.5% were at heightened risk of SCD events, which can be used for risk assessment. Risk stratifications for SCD, combining clinical and cardiac magnetic resonance imaging may potentially guide decision-making for implantable cardioverter defibrillator therapy.
Approximations to the Fisher Information Metric of Deep Generative Models for Out-Of-Distribution Detection
Likelihood-based deep generative models such as score-based diffusion models and variational autoencoders are state-of-the-art machine learning models approximating high-dimensional distributions of data such as images, text, or audio. One of many downstream tasks they can be naturally applied to is out-of-distribution (OOD) detection. However, seminal work by Nalisnick et al. which we reproduce showed that deep generative models consistently infer higher log-likelihoods for OOD data than data they were trained on, marking an open problem. In this work, we analyse using the gradient of a data point with respect to the parameters of the deep generative model for OOD detection, based on the simple intuition that OOD data should have larger gradient norms than training data. We formalise measuring the size of the gradient as approximating the Fisher information metric. We show that the Fisher information matrix (FIM) has large absolute diagonal values, motivating the use of chi-square distributed, layer-wise gradient norms as features. We combine these features to make a simple, model-agnostic and hyperparameter-free method for OOD detection which estimates the joint density of the layer-wise gradient norms for a given data point. We find that these layer-wise gradient norms are weakly correlated, rendering their combined usage informative, and prove that the layer-wise gradient norms satisfy the principle of (data representation) invariance. Our empirical results indicate that this method outperforms the Typicality test for most deep generative models and image dataset pairings.
From Big Data to the Clinic: Methodological and Statistical Enhancements to Implement the UK Biobank Imaging Framework in a Memory Clinic.
The analysis tools and statistical methods used in large neuroimaging research studies differ from those applied in clinical contexts, making it unclear whether these techniques can be translated to a memory clinic setting. The Oxford Brain Health Clinic (OBHC) was established in 2020 to bridge this gap between research studies and memory clinics. We optimised the UK Biobank imaging framework for the memory clinic setting by integrating enhanced quality control (QC) processes (MRIQC, QUAD, and DSE decomposition) and supplementary dementia-informed analyses (lobar volumes, NBM volumes, WMH classification, PSMD, cortical diffusion MRI metrics, and tract volumes) into the analysis pipeline. We explored associations between resultant imaging-derived phenotypes (IDPs) and clinical phenotypes in the OBHC patient population (N = 213), applying hierarchical FDR correction to account for multiple testing. 14%-24% of scans were flagged by automated QC tools, but upon visual inspection, only 0%-2.4% of outputs were excluded. The pipeline successfully generated 5683 IDPs aligned with UK Biobank and 110 IDPs targeted towards dementia-related changes. We replicated established associations and found novel associations between brain metrics and age, cognition, and dementia-related diagnoses. The imaging protocol is feasible, acceptable, and yields high-quality data that is usable for both clinical and research purposes. We validated the use of this methodology in a real-world memory clinic population, which demonstrates the potential of this enhanced pipeline to bridge the gap between big data studies and clinical settings.
Long-term outcomes of bilateral salpingo-oophorectomy in women with personal history of breast cancer.
OBJECTIVES: To investigate the association between bilateral salpingo-oophorectomy (BSO) and long-term health outcomes in women with a personal history of breast cancer. METHODS AND ANALYSIS: We used data on women diagnosed with invasive breast cancer between 1995 and 2019 from the National Cancer Registration Dataset (NCRD) in England. The data were linked to the Hospital Episode Statistics-Admitted Patient Care dataset to identify BSO delivery. Long-term health outcomes were selected from both datasets. Multivariable Cox regression was used to examine the associations, with BSO modelled as a time-dependent covariate. The associations were investigated separately by age at BSO. RESULTS: We identified 568 883 women, 23 401 of whom had BSO after the breast cancer diagnosis. There was an increased risk of total cardiovascular diseases with an HR of 1.10 (95% CI 1.04 to 1.16) in women who had BSO<55 years and 1.07 (95% CI 1.01 to 1.13) for women who had BSO≥55 years. There was an increased risk of ischaemic heart diseases, but there was no association with cerebrovascular diseases. BSO at any age was associated with an increased risk of depression (HR 1.20, 95% CI 1.12 to 1.28) and increased risk of second non-breast cancer in older women (HR 1.21, 95%CI 1.08 to 1.35). BSO in older women was associated with reduced risk of all-cause mortality (HR 0.92, 95% CI 0.87 to 096), but not in women who had BSO<55 years. CONCLUSION: In women with a personal history of breast cancer, BSO before and after the age of 55 years is associated with an increased risk of long-term outcomes. BSO after 55 years is associated with reduced all-cause mortality. Family history or genetic predisposition may confound these associations.
Convalescent plasma in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial
ABSTRACT Background Treatment of COVID-19 patients with plasma containing anti-SARS-CoV-2 antibodies may have a beneficial effect on clinical outcomes. We aimed to evaluate the safety and efficacy of convalescent plasma in patients admitted to hospital with COVID-19. Methods In this randomised, controlled, open-label, platform trial (Randomised Evaluation of COVID-19 Therapy [RECOVERY]) several possible treatments are being compared with usual care in patients hospitalised with COVID-19 in the UK. Eligible and consenting patients were randomly allocated to receive either usual care plus high titre convalescent plasma or usual care alone. The primary outcome was 28-day mortality. Findings Between 28 May 2020 and 15 January 2021, 5795 patients were randomly allocated to receive convalescent plasma and 5763 to usual care alone. There was no significant difference in 28-day mortality between the two groups: 1398 (24%) of 5795 patients allocated convalescent plasma and 1408 (24%) of 5763 patients allocated usual care died within 28 days (rate ratio [RR] 1·00; 95% confidence interval [CI] 0·93 to 1·07; p=0·93). The 28-day mortality rate ratio was similar in all prespecified subgroups of patients, including in those patients without detectable SARS-CoV-2 antibodies at randomisation. Allocation to convalescent plasma had no significant effect on the proportion of patients discharged from hospital within 28 days (66% vs . 67%; rate ratio 0·98; 95% CI 0·94-1·03, p=0·50). Among those not on invasive mechanical ventilation at baseline, there was no significant difference in the proportion meeting the composite endpoint of progression to invasive mechanical ventilation or death (28% vs . 29%; rate ratio 0·99; 95% CI 0·93-1·05, p=0·79). Interpretation Among patients hospitalised with COVID-19, high-titre convalescent plasma did not improve survival or other prespecified clinical outcomes. Funding UK Research and Innovation (Medical Research Council) and National Institute of Health Research (Grant refs: MC_PC_19056; COV19-RECPLA).