Clinical perceptions of radiation therapy undergraduate competency standards

Introduction The multifactorial nature of clinical skills development makes assessment of undergraduate radiation therapist competence level by clinical mentors challenging. A recent overhaul of the clinical assessment strategy at Queensland University of Technology has moved away from the high-stakes Observed Structured Clinical Examination (OSCE) to encompass a more continuous measure of competence. This quantitative study aimed to gather stakeholder evidence to inform development of standards by which to measure student competence for a range of levels of progression. Methods A simple anonymous questionnaire was distributed to all Queensland radiation therapists. The tool asked respondents to assign different levels of competency with a range of clinical tasks to different levels of student. All data were anonymous and was combined for analysis using Microsoft Excel. Results Feedback indicated good agreement with tasks that specified amount of direction required and this has been incorporated into the new clinical achievements record that the students need to have signed off. Additional puzzling findings suggested higher expectations with planning tasks than with treatment-based tasks. Conclusion The findings suggest that the amount of direction required by students is a valid indicator of their level and has been adopted into the clinical assessment scheme. Further work will build on this to further define standards of competency for undergraduates.

Clinical data warehousing : a business analytics approach for managing health data

Heterogeneous health data is a critical issue when managing health information for quality decision making processes. In this paper we examine the efficient aggregation of lifestyle information through a data warehousing architecture lens. We present a proof of concept for a clinical data warehouse architecture that enables evidence based decision making processes by integrating and organising disparate data silos in support of healthcare services improvement paradigms.

The visual and functional impacts of astigmatism and its clinical management

Purpose: To provide a comprehensive overview of research examining the impact of astigmatism on clinical and functional measures of vision, the short and longer term adaptations to astigmatism that occur in the visual system, and the currently available clinical options for the management of patients with astigmatism. Recent findings: The presence of astigmatism can lead to substantial reductions in visual performance in a variety of clinical vision measures and functional visual tasks. Recent evidence demonstrates that astigmatic blur results in short-term adaptations in the visual system that appear to reduce the perceived impact of astigmatism on vision. In the longer term, uncorrected astigmatism in childhood can also significantly impact on visual development, resulting in amblyopia. Astigmatism is also associated with the development of spherical refractive errors. Although the clinical correction of small magnitudes of astigmatism is relatively straightforward, the precise, reliable correction of astigmatism (particularly high astigmatism) can be challenging. A wide variety of refractive corrections are now available for the patient with astigmatism, including spectacle, contact lens and surgical options. Conclusion: Astigmatism is one of the most common refractive errors managed in clinical ophthalmic practice. The significant visual and functional impacts of astigmatism emphasise the importance of its reliable clinical management. With continued improvements in ocular measurement techniques and developments in a range of different refractive correction technologies, the future promises the potential for more precise and comprehensive correction options for astigmatic patients.

Clinical trials in pediatrics

“Children are not little adults.” Almost all aspects of children's health including clinical trials and drug development are poor cousins of adult health. Over the years, pediatricians worldwide caution against blind extrapolation of adult data to children as it may result in considerable harm [1,2]. Also, it is increasingly recognized that the roots of many chronic diseases in adulthood stem from childhood and tackling health issues in children lead to improved health in adults [3,4]. Furthermore, investment in early childhood has long-term benefits in adults not only in health but also in other aspects of life such as education and crime reduction [4,5]. Arguably, health at birth is the single most important predictor of health in adulthood as the inequality of an infant at birth has intergenerational effects [5]. The Carolina Abecedarian Project showed that early childhood programs that are of high quality result in substantial societal benefits (e.g., reduction of crime, increased earnings, better education) [4,5]. A recent publication from this project found that this benefit also translated into improved adult health outcomes [4]. In a randomized trial, Campbell and colleagues described that disadvantaged children who were randomized to the intervention group (early education, health screenings and nutrition program) had significantly lower rates of metabolic syndrome, obesity and hypertension, when aged in their mid-30s, compared with the control group [4]...

The relationship between emotional intelligence and depression in a clinical sample

Background and Objectives: Although depression is a commonly occurring mental illness, research concerning strategies for early detection and prophylaxis has not until now focused on the possible utility of measures of Emotional Intelligence (EI) as a potential predictive factor. The current study aimed to investigate the relationship between EI and a clinical diagnosis of depression in a cohort of adults. Methods: Sixty-two patients (59.70% female) with a DSM-IV-TR diagnosis of a major affective disorder and 39 aged matched controls (56.40% female) completed self-report instruments assessing EI and depression in a cross-sectional study. Results: Significant associations were observed between severity of depression and the EI dimensions of Emotional Management (r = -0.56) and Emotional Control (r = -0.62). The results show a reduced social involvement, an increased prior institutionalization and an increased incidence of "Schizophrenic Psychosis" and "Abnormal Personalities" in the sub-group of repeated admissions. Conclusions: Measures of EI may have predictive value in terms of early identification of those at risk for developing depression. The current study points to the potential value of conducting further studies of a prospective nature.

Australia’s first Allied Health Prescribing Program – exploring participants' understanding and confidence in clinical therapy choices for patient management

BACKGROUND Globally there are emerging trends for non-medical health professionals to expand their scope of practice into prescribing. The NPS Prescribing Competencies Framework and the Health Professionals Prescribing Pathway Program are recent initiatives to assist with implementation of prescribing for allied health professionals (AHPs). For AHPs to become prescribers, training programmes must be designed to extend their knowledge of medicines information and medicine management principles with the aim of optimising medicines related outcomes for patients. AIM To explore the understanding and confidence in clinical therapeutic choices for patient management of those AHPs enrolled in the Allied Health Prescribing Training Program Module One: Introduction to clinical therapeutics for prescribers, delivered by Queensland University of Technology, Brisbane. METHOD A pre-post survey was developed to explore key themes around understanding and confidence in selecting therapeutic choices for patients with varying complexities of conditions. Data were collected from participants in week one and 13 of the module via an online survey using a five-point Likert scale (1 = Strongly Agree (SA) to 5 = Strongly Disagree (SD)). RESULTS In the pre-Module survey the AHPs had a limited degree (D/SD) of understanding and confidence regarding the safe and effective use of medicines and appropriate therapeutic choices for managing patients, particularly with complex patients. This improved significantly in the post Module survey (A/SA).

“I would have lost the respect of my friends and family if they knew I had bent the road rules” : parents, peers, and the perilous behaviour of young drivers

Young novice drivers are at considerable risk of injury on the road. Their behaviour appears vulnerable to the social influence of their parents and friends. The nature and mechanisms of parent and peer influence on young novice driver (16–25 years) behaviour was explored via small group interviews (n = 21) and two surveys (n1 = 1170, n2 = 390) to inform more effective young driver countermeasures. Parental and peer influence occurred in preLicence, Learner, and Provisional (intermediate) periods. Pre-Licence and unsupervised Learner drivers reported their parents were less likely to punish risky driving (e.g., speeding). These drivers were more likely to imitate their parents and reported their parents were also risky drivers. Young novice drivers who experienced or expected social punishments from peers, including ‘being told off’ for risky driving, reported less riskiness. Conversely drivers who experienced or expected social rewards such as being ‘cheered on’ by friends – who were also more risky drivers – reported more risky driving including crashes and offences. Interventions enhancing positive influence and curtailing negative influence may improve road safety outcomes not only for young novice drivers, but for all persons who share the road with them. Parent-specific interventions warrant further development and evaluation including: modelling safe driving behaviour by parents; active monitoring of driving during novice licensure; and sharing the family vehicle during the intermediate phase. Peer-targeted interventions including modelling of safe driving behaviour and attitudes; minimisation of social reinforcement and promotion of social sanctions for risky driving also need further development and evaluation.

Wheeze, asthma diagnosis and medication use : a national adult survey in a developing country

Background As relatively little is known about adult wheeze and asthma in developing countries, this study aimed to determine the predictors of wheeze, asthma diagnosis, and current treatment in a national survey of South African adults. Methods A stratified national probability sample of households was drawn and all adults (>14 years) in the selected households were interviewed. Outcomes of interest were recent wheeze, asthma diagnosis, and current use of asthma medication. Predictors of interest were sex, age, household asset index, education, racial group, urban residence, medical insurance, domestic exposure to smoky fuels, occupational exposure, smoking, body mass index, and past tuberculosis. Results A total of 5671 men and 8155 women were studied. Although recent wheeze was reported by 14.4% of men and 17.6% of women and asthma diagnosis by 3.7% of men and 3.8% of women, women were less likely than men to be on current treatment (OR 0.6; 95% confidence interval (CI) 0.5 to 0.8). A history of tuberculosis was an independent predictor of both recent wheeze (OR 3.4; 95% CI 2.5 to 4.7) and asthma diagnosis (OR 2.2; 95% CI 1.5 to 3.2), as was occupational exposure (wheeze: OR 1.8; 95% CI 1.5 to 2.0; asthma diagnosis: OR 1.9; 95% CI 1.4 to 2.4). Smoking was associated with wheeze but not asthma diagnosis. Obesity showed an association with wheeze only in younger women. Both wheeze and asthma diagnosis were more prevalent in those with less education but had no association with the asset index. Independently, having medical insurance was associated with a higher prevalence of diagnosis. Conclusions Some of the findings may be to due to reporting bias and heterogeneity of the categories wheeze and asthma diagnosis, which may overlap with post tuberculous airways obstruction and chronic obstructive pulmonary disease due to smoking and occupational exposures. The results underline the importance of controlling tuberculosis and occupational exposures as well as smoking in reducing chronic respiratory morbidity. Validation of the asthma questionnaire in this setting and research into the pathophysiology of post tuberculous airways obstruction are also needed.

Design, construction and evaluation of systems to predict risk in obstetrics

We present a systematic, practical approach to developing risk prediction systems, suitable for use with large databases of medical information. An important part of this approach is a novel feature selection algorithm which uses the area under the receiver operating characteristic (ROC) curve to measure the expected discriminative power of different sets of predictor variables. We describe this algorithm and use it to select variables to predict risk of a specific adverse pregnancy outcome: failure to progress in labour. Neural network, logistic regression and hierarchical Bayesian risk prediction models are constructed, all of which achieve close to the limit of performance attainable on this prediction task. We show that better prediction performance requires more discriminative clinical information rather than improved modelling techniques. It is also shown that better diagnostic criteria in clinical records would greatly assist the development of systems to predict risk in pregnancy. We present a systematic, practical approach to developing risk prediction systems, suitable for use with large databases of medical information. An important part of this approach is a novel feature selection algorithm which uses the area under the receiver operating characteristic (ROC) curve to measure the expected discriminative power of different sets of predictor variables. We describe this algorithm and use it to select variables to predict risk of a specific adverse pregnancy outcome: failure to progress in labour. Neural network, logistic regression and hierarchical Bayesian risk prediction models are constructed, all of which achieve close to the limit of performance attainable on this prediction task. We show that better prediction performance requires more discriminative clinical information rather than improved modelling techniques. It is also shown that better diagnostic criteria in clinical records would greatly assist the development of systems to predict risk in pregnancy.

The clinical utility of the Cornell Scale for Depression in Dementia as a routine assessment in nursing homes

Objective To examine the clinical utility of the Cornell Scale for Depression in Dementia (CSDD) in nursing homes. Setting 14 nursing homes in Sydney and Brisbane, Australia. Participants 92 residents with a mean age of 85 years. Measurements Consenting residents were assessed by care staff for depression using the CSDD as part of their routine assessment. Specialist clinicians conducted assessment of depression using the Semi-structured Clinical Diagnostic Interview for DSM-IV-TR Axis I Disorders for residents without dementia or the Provisional Diagnostic Criteria for Depression in Alzheimer Disease for residents with dementia to establish expert clinical diagnoses of depression. The diagnostic performance of the staff completed CSDD was analyzed against expert diagnosis using receiver operating characteristic (ROC) curves. Results The CSDD showed low diagnostic accuracy, with areas under the ROC curve being 0.69, 0.68 and 0.70 for the total sample, residents with dementia and residents without dementia, respectively. At the standard CSDD cutoff score, the sensitivity and specificity were 71% and 59% for the total sample, 69% and 57% for residents with dementia, and 75% and 61% for residents without dementia. The Youden index (for optimizing cut-points) suggested different depression cutoff scores for residents with and without dementia. Conclusion When administered by nursing home staff the clinical utility of the CSDD is highly questionable in identifying depression. The complexity of the scale, the time required for collecting relevant information, and staff skills and knowledge of assessing depression in older people must be considered when using the CSDD in nursing homes.

Standardising and assessing digital images for use in clinical trials : a practical, reproducible method that blinds the assessor to treatment allocation

With the increasing availability of high quality digital cameras that are easily operated by the non-professional photographer, the utility of using digital images to assess endpoints in clinical research of skin lesions has growing acceptance. However, rigorous protocols and description of experiences for digital image collection and assessment are not readily available, particularly for research conducted in remote settings. We describe the development and evaluation of a protocol for digital image collection by the non-professional photographer in a remote setting research trial, together with a novel methodology for assessment of clinical outcomes by an expert panel blinded to treatment allocation.

The BAD project: data mining, database and prediction of protein adsorption on surfaces

Protein adsorption at solid-liquid interfaces is critical to many applications, including biomaterials, protein microarrays and lab-on-a-chip devices. Despite this general interest, and a large amount of research in the last half a century, protein adsorption cannot be predicted with an engineering level, design-orientated accuracy. Here we describe a Biomolecular Adsorption Database (BAD), freely available online, which archives the published protein adsorption data. Piecewise linear regression with breakpoint applied to the data in the BAD suggests that the input variables to protein adsorption, i.e., protein concentration in solution; protein descriptors derived from primary structure (number of residues, global protein hydrophobicity and range of amino acid hydrophobicity, isoelectric point); surface descriptors (contact angle); and fluid environment descriptors (pH, ionic strength), correlate well with the output variable-the protein concentration on the surface. Furthermore, neural network analysis revealed that the size of the BAD makes it sufficiently representative, with a neural network-based predictive error of 5% or less. Interestingly, a consistently better fit is obtained if the BAD is divided in two separate sub-sets representing protein adsorption on hydrophilic and hydrophobic surfaces, respectively. Based on these findings, selected entries from the BAD have been used to construct neural network-based estimation routines, which predict the amount of adsorbed protein, the thickness of the adsorbed layer and the surface tension of the protein-covered surface. While the BAD is of general interest, the prediction of the thickness and the surface tension of the protein-covered layers are of particular relevance to the design of microfluidics devices.