Twelve tips for optimising medical student retention of anatomy.

Concern that many graduate medical students do not know sufficient anatomy to safely and effectively assess and treat patients is a frequent complaint by clinicians. Although downgrading of anatomy relative to newer basic sciences is often blamed, there is evidence students rapidly forget anatomy. However, there are a number of ways instructors can foster long-term retention of anatomy, the most powerful involving intertwining clinical and anatomical information and assessing in-depth processing. Assisting this process is 'triaging' the curriculum so it contains only clinically engaged anatomy. Students are far more likely to remember information which they consider to be relevant to their future vocation. Therefore, teaching only anatomy which is likely to be useful in a clinical context tends to improve long-term retention of anatomy by medical students. Other helpful techniques include incorporating surface and radiological anatomy in a vertically integrated curriculum, reciprocal peer teaching and employing clinically qualified instructors.

Evaluation of DSM-5 and IWG-2 criteria for the diagnosis of Alzheimer's disease and dementia with Lewy bodies.

Despite differing target audiences and scope it is possible to compare the Fifth Edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) [American Psychiatric Association: Diagnostic and Statistical Manual of Mental Disorders, 5th ed. Arlington: American Psychiatric Association, 2013] and the Second International Working Group for New Research Criteria for the Diagnosis of Alzheimer's Disease (IWG-2) [Dubois B, Feldman HH, Jacova C, Hampel H, Molinuevo JL, Blennow K, et al. Advancing research diagnostic criteria for Alzheimer's disease: the IWG-2 criteria. Lancet Neurol 2014;13:614-29] diagnostic criteria for both Alzheimer's disease (AD) and dementia with Lewy bodies (DLB). With regard to the diagnosis of AD the principal difference is the inclusion of biomarkers in the IWG-2 diagnostic criteria for this condition. This creates a number of difficulties including a lack of regulatory approval, cultural and other objections to the collection of cerebrospinal fluid (CSF), and a lack of facilities for collection and analysis restricting analysis of CSF proteins to larger tertiary centres [Dubois B, Feldman HH, Jacova C, Hampel H, Molinuevo JL, Blennow K, et al. Advancing research diagnostic criteria for Alzheimer's disease: the IWG-2 criteria. Lancet Neurol 2014;13:614-29]. With regard to diagnostic criteria for DLB, IWG-2 research criteria designate the co-occurrence of AD and DLB as 'mixed AD'. However, Alzheimer's type pathology (ADTP) and Lewy body pathology frequently occur together rendering a separate 'mixed AD' category superfluous. The reality is that routine clinical diagnosis of AD and DLB will continue to be based on a thorough general and neurological examination indicating a preponderance of signs and symptoms for one or other of these conditions [Seeley WW, Miller BL. Alzheimer's disease and other dementias. In: Hauser SL, Josephson SA, editors. Harrison's neurology in clinical medicine, 3rd ed. New York: McGraw Hill, 2013]. Similarly, AD and DLB research will continue to primarily depend on clinically focussed DSM-5 criteria, making DSM-5 superior to IWG-2 in both clinical and research settings.

Dependence Independence Measure for Posterior and Anterior EMG Sensors Used in Simple and Complex Finger Flexion Movements: Evaluation Using SDICA.

Identification of simple and complex finger flexion movements using surface electromyography (sEMG) and a muscle activation strategy is necessary to control human-computer interfaces such as prosthesis and orthoses. In order to identify these movements, sEMG sensors are placed on both anterior and posterior muscle compartments of the forearm. In general, the accuracy of myoelectric classification depends on several factors, which include number of sensors, features extraction methods, and classification algorithms. Myoelectric classification using a minimum number of sensors and optimal electrode configuration is always a challenging task. Sometimes, using several sensors including high density electrodes will not guarantee high classification accuracy. In this research, we investigated the dependence and independence nature of anterior and posterior muscles during simple and complex finger flexion movements. The outcome of this research shows that posterior parts of the hand muscles are dependent and hence responsible for most of simple finger flexion. On the other hand, this study shows that anterior muscles are responsible for most complex finger flexion. This also indicates that simple finger flexion can be identified using sEMG sensors connected only on anterior muscles (making posterior placement either independent or redundant), and vice versa is true for complex actions which can be easily identified using sEMG sensors on posterior muscles. The result of this study is beneficial for optimal electrode configuration and design of prosthetics and other related devices using a minimum number of sensors.

Assessment of the upper limb in acute stroke: the validity of hierarchal scoring for the Motor Assessment Scale.

BACKGROUND/AIM: Stroke is the greatest contributor to disability in Australian adults and much of this disability results from a stroke-affected upper limb. This study aimed to determine the validity of hierarchal scoring for the upper limb subscale of the Motor Assessment Scale (UL-MAS) in acute stroke using Rasch analysis. METHOD: This study applied Rasch analysis to 40 UL-MAS assessment results across 25 subjects to determine the validity of the hierarchy of the three upper limb subsets: upper arm function (six), hand movements (seven) and advanced hand activities (eight). Rasch analysis examines the relationship between 'item difficulty' and 'person ability' and produces an output which represents the difficulty of each item in relation to each other. RESULTS: As hypothesised, the hierarchy was upheld within subset 6. In subset 7, the hierarchy was not upheld. Results indicated that item 3 was the least difficult, followed by items 1, 4, 2, 5 and 6 in order of increasing difficulty. In subset 8 the hierarchy was not upheld. Results indicated that item 1 was the least difficult, followed by item 6, then 2 and 5 of equal value and then 3 and 4 of equal value. CONCLUSIONS: The hierarchal scoring is not supported for subsets 7 and 8 and future research is required to explore the validity of alternate scoring methods. At present, the authors recommend that the UL-MAS should be scored non-hierarchally, meaning that every item within the subsets should be scored regardless of its place within the hierarchy (UL-MAS-NH).