Learning outcomes

BACKGROUND: The provision of graded supervision affording progressive autonomy is fundamental to the progression of a medical learner toward competency for independent practice; the decision of how much supervision versus autonomy to provide a trainee in the execution of clinical care constitutes an entrustment decision. Despite entrustment decision making occurring both daily in practice and summatively at points of matriculation through stages of medical training, the factors influencing entrustment decisions remain poorly understood across clinical contexts...

INTRODUCTION: When parents of dependent children are treated in psychiatric inpatient hospital, it typically involves separation of parent and child for the duration of treatment, which can be highly distressing to the dyad and can result in disruption to the parent-child relationship. Parents who have experienced hospitalisation have expressed a desire for their parenting identity to be recognized and appropriately engaged with during their treatment. This recognition includes provision of interventions which support them as parents to limit the impact of their mental health on their children...

PURPOSE: Empowering parents to deliver evidenced-based interventions improves outcomes for children with or infants at risk of cerebral palsy (CP), by integrating repetition and contextual learning into daily routines. We aimed to identify the barriers and facilitators to parent-delivered interventions and suggest practice improvements guided by behaviour change models. METHODS: Eight electronic databases were searched to identify studies presenting parent and therapist perspectives on parent-delivered interventions in CP...

Despite the abundance of genotype-phenotype association studies, the resulting association outcomes often lack robustness and interpretations. To address these challenges, we introduce PheSeq, a Bayesian deep learning model that enhances and interprets association studies through the integration and perception of phenotype descriptions. By implementing the PheSeq model in three case studies on Alzheimer's disease, breast cancer, and lung cancer, we identify 1024 priority genes for Alzheimer's disease and 818 and 566 genes for breast cancer and lung cancer, respectively...

BACKGROUND: Embedding researchers into policy and other settings may enhance research capacity within organisations to enable them to become more research active. We aimed to generate an evidence map on evaluations of embedded researcher interventions to (i) identify where systematic reviews and primary research are needed and (ii) develop conceptual understandings of 'embedded researchers'. We define 'embedded researchers' through a set of principles that incorporate elements such as the aim of activities, the types of relationships and learning involved, and the affiliations and identities adopted...

Accurately predicting patients' risk for specific medical outcomes is paramount for effective healthcare management and personalized medicine. While a substantial body of literature addresses the prediction of diverse medical conditions, existing models predominantly focus on singular outcomes, limiting their scope to one disease at a time. However, clinical reality often entails patients concurrently facing multiple health risks across various medical domains. In response to this gap, our study proposes a novel multi-risk framework adept at simultaneous risk prediction for multiple clinical outcomes, including diabetes, mortality, and hypertension...

OBJECTIVES: To build self-supervised foundation models for multicontrast MRI of the whole brain and evaluate their efficacy in assisting diagnosis of brain tumors. METHODS: In this retrospective study, foundation models were developed using 57,621 enhanced head MRI scans through self-supervised learning with a pretext task of cross-contrast context restoration with two different content dropout schemes. Downstream classifiers were constructed based on the pretrained foundation models and fine-tuned for brain tumor detection, discrimination, and molecular status prediction...

BACKGROUND: Globally, cardiovascular disease (CVD) remains the leading cause of death, warranting effective management and prevention measures. Risk prediction tools are indispensable for directing primary and secondary prevention strategies for CVD and are critical for estimating CVD risk. Machine learning (ML) methodologies have experienced significant advancements across numerous practical domains in recent years. Several ML and statistical models predicting CVD time-to-event outcomes have been developed...

INTRODUCTION: Approximately 250 million children under 5 years of age are at risk of poor development in low-income and middle-income countries. However, existing early childhood development (ECD) interventions can be expensive, labour intensive and challenging to deliver at scale. Mass media may offer an alternative approach to ECD intervention. This protocol describes the planned economic evaluation of a cluster-randomised controlled trial of a radio campaign promoting responsive caregiving and opportunities for early learning during the first 3 years of life in rural Burkina Faso ( SUNRISE trial)...

OBJECTIVE: Entrustable professional activities (EPAs) have seen widespread adoption in medical education and other health professions education. EPAs aim to provide a bridge between competency-based education and clinical practice by translating competencies into fundamental profession-specific tasks associated with clinical practice. Despite the extensive use of EPAs in health professions education, EPAs have yet to be introduced into chiropractic education. The purpose of this paper is to describe the development and introduction of EPAs as part of 2 community-based chiropractic student preceptorship education programs in the United States...

Evaluating the rehabilitation status of individuals with serious mental illnesses (SMI) necessitates a comprehensive analysis of multimodal data, including unstructured text records and structured diagnostic data. However, progress in the effective assessment of rehabilitation status remains limited. Our study develops a deep learning model integrating Bidirectional Encoder Representations from Transformers (BERT) and TabNet through a late fusion strategy to enhance rehabilitation prediction, including referral risk, dangerous behaviors, self-awareness, and medication adherence, in patients with SMI...

Expanding loco-regional nodes harvesting is expected to increase survival. This improvement may be associated to stage migration (SM). However, the great bulk of harvested lymph nodes observed in large dissections is negative. M&M: 830 patients who received R0 gastrectomy for adenocarcinoma were included. pN+ patients with <26 nodes (n = 209) were included for a simulation to "offer 26 nodes" - SM (proportional and exponential based) was simulated and analysed through machine learning algorithms...

BACKGROUND: Endoscopic surgery has shown promise in treating Spontaneous Intracerebral Hemorrhage (sICH), but its adoption in county-level hospitals has been hindered by the high level of surgical expertise required. METHODS: In this retrospective study at a county hospital, we utilized a Cumulative Sum (CUSUM) control chart to visualize the learning curve for two neurosurgeons. We compared patient outcomes in the learning and proficient phases, and compared them with expected outcomes based on ICH score and ICH functional outcome score, respectively...

BACKGROUND: Cardiovascular patients experience high rates of adverse outcomes following discharge from hospital, which may be preventable through early identification and targeted action. This study aimed to investigate the effectiveness and explainability of machine learning algorithms in predicting unplanned readmission and death in cardiovascular patients at 30 days and 180 days from discharge. METHODS: Gradient boosting machines were trained and evaluated using data from hospital electronic medical records linked to hospital administrative and mortality data for 39,255 patients admitted to four hospitals in New South Wales, Australia between 2017 and 2021...

OBJECTIVE: The aim of this study was to determine whether a flipped classroom curriculum coupled with case-based learning would improve residents' perceptions of the learning environment, improve education outcomes, and increase faculty engagement. Research suggests that active learning yields better educational results compared with passive learning. However, faculty are more comfortable providing lectures that require only passive participation from learners. METHODS: A council was created to identify issues with the current format of the resident didactic curriculum and to redesign the neurosurgical curriculum and conference per Accreditation Council for Graduate Medical Education (ACGME) requirements...

BACKGROUND: Patients with spinal cord injuries (SCI) experience variable urinary symptoms and QOL. Our objective was to use machine learning to identify bladder-relevant phenotypes after SCI and assess their association with urinary symptoms and QOL. METHODS: We used data from the Neurogenic Bladder Research Group SCI (NBRG) registry. Baseline variables that were previously shown to be associated with bladder symptoms/QOL were included in the machine learning environment...

Animal psychophysics can generate rich behavioral datasets, often comprised of many 1000s of trials for an individual subject. Gradient-boosted models are a promising machine learning approach for analyzing such data, partly due to the tools that allow users to gain insight into how the model makes predictions. We trained ferrets to report a target word's presence, timing, and lateralization within a stream of consecutively presented non-target words. To assess the animals' ability to generalize across pitch, we manipulated the fundamental frequency (F0) of the speech stimuli across trials, and to assess the contribution of pitch to streaming, we roved the F0 from word token-to-token...

Background Bacterial vaginosis (BV) is the most common cause of vaginal discharge in reproductive age women; however, little is known about it after menopause. We aimed to learn more about BV in Australian postmenopausal women. Methods We conducted an online survey (July-September 2021). Participants were recruited via social media and professional networks and asked about demographic characteristics, sexual history and BV experiences. Outcomes of interest were the proportion who had heard of BV, had BV ever, or had BV after menopause...

OBJECTIVE: Machine learning (ML) is increasingly employed to diagnose medical conditions, with algorithms trained to assign a single label using a black-box approach. We created an ML approach using deep learning that generates outcomes that are transparent and in line with clinical, diagnostic rules. We demonstrate our approach for autism spectrum disorders (ASD), a neurodevelopmental condition with increasing prevalence. METHODS: We use unstructured data from the Centers for Disease Control and Prevention (CDC) surveillance records labeled by a CDC-trained clinician with ASD A1-3 and B1-4 criterion labels per sentence and with ASD cases labels per record using Diagnostic and Statistical Manual of Mental Disorders (DSM5) rules...

Magnetic Resonance Imaging (MRI) datasets from epidemiological studies often show a lower prevalence of motion artifacts than what is encountered in clinical practice. These artifacts can be unevenly distributed between subject groups and studies which introduces a bias that needs addressing when augmenting data for machine learning purposes. Since unreconstructed multi-channel k-space data is typically not available for population-based MRI datasets, motion simulations must be performed using signal magnitude data...