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Radiotherapy for pediatric brain tumor has been associated with late cognitive effects. Compared to conventional photon radiotherapy (XRT), proton radiotherapy (PRT) delivers less radiation to healthy brain tissue. PRT has been associated with improved long term cognitive outcomes compared to XRT. However, there is limited research comparing the effects of XRT and PRT on verbal memory outcomes.
Participants and Methods:
Survivors of pediatric brain tumor treated with either XRT (n = 29) or PRT (n = 51) completed neuropsychological testing > 1 year following radiotherapy. XRT and PRT groups were similar with respect to sex, handedness, race, age at diagnosis, age at evaluation, tumor characteristics, and treatment history (i.e., craniospinal irradiation, craniotomy, shunting, chemotherapy, radiation dose). Verbal learning and memory were assessed using the age-appropriate version of the California Verbal Learning Test (CVLT-II/CVLT-C). Measures of intellectual functioning, executive functioning, attention and adaptive behavior were also collected. Performance on neuropsychological measures was compared between treatment groups (XRT vs. PRT) using analysis of covariance (ANCOVA). On the CVLT, each participant was classified as having an encoding deficit profile (i.e., impaired learning, recall, and recognition), retrieval deficit profile (i.e., impaired recall but intact recognition), intact profile, or other profile. Chi-squared tests of independence were used to compare the probability of each memory profile between treatment groups. Pearson correlation was used to examine associations between memory performance and strategy use, intellectual functioning, adaptive behavior, attention, and executive functioning.
Results:
Overall, patients receiving PRT demonstrated superior verbal learning (CVLT Trials 1-5; t(76) = 2.61, p = .011), recall (CVLT Long Delay Free; t(76) = 3.57, p = .001) and strategy use (CVLT Semantic Clustering; t(76) = 2.29, p = .025) compared to those treated with XRT. Intact performance was more likely in the PRT group than the XRT group (71% PRT, 38% XRT; X2 = 8.14, p = .004). Encoding and retrieval deficits were both more common in the XRT group, with encoding problems being most prevalent (Encoding Deficits: 31% XRT, 12% PRT, X2 = 4.51, p = .034; Retrieval Deficits: 17% XRT, 4% PRT, X2 = 4.11, p = .043). Across all participants, semantic clustering predicted better encoding (r = .28, p = .011) and retrieval (r = .26, p = .022). Better encoding predicted higher intellectual (r = .56, p < .001) and adaptive functioning (r = .30, p = .011), and fewer parent-reported concerns about day-today attention (r = -.36, p = .002), and cognitive regulation (r = -.35, p = .002).
Conclusions:
Results suggest that PRT is associated with superior verbal memory outcomes compared to XRT, which may be driven by encoding skills and use of learning strategies. Moreover, encoding ability predicted general intellectual ability and day-to-day functioning. Future work may help to clarify underlying neural mechanisms associated with verbal memory decline following radiotherapy, which will better inform treatment approaches for survivors of pediatric brain tumor.
In recent years, there has been significant momentum in applying deep learning (DL) to machine health monitoring (MHM). It has been widely claimed that DL methodologies are superior to more traditional techniques in this area. This paper aims to investigate this claim by analysing a real-world dataset of helicopter sensor faults provided by Airbus. Specifically, we will address the problem of machine sensor health unsupervised classification. In a 2019 worldwide competition hosted by Airbus, Fujitsu Systems Europe (FSE) won first prize by achieving an F1-score of 93% using a DL model based on generative adversarial networks (GAN). In another comprehensive study, various modified and existing image encoding methods were compared for the convolutional auto-encoder (CAE) model. The best classification result was achieved using the scalogram as the image encoding method, with an F1-score of 91%. In this paper, we use these two studies as benchmarks to compare with basic statistical analysis methods and the one-class supporting vector machine (SVM). Our comparative study demonstrates that while DL-based techniques have great potential, they are not always superior to traditional methods. We therefore recommend that all future published studies of applying DL methods to MHM include appropriately selected traditional reference methods, wherever possible.
To characterize the features of aged care users who died by suicide and examine the use of mental health services and psychopharmacotherapy in the year before death.
Design:
Population-based, retrospective exploratory study
Setting and participants:
Individuals who died while accessing or waiting for permanent residential aged care (PRAC) or home care packages in Australia between 2008 and 2017.
Measurements:
Linked datasets describing aged care use, date and cause of death, health care use, medication use, and state-based hospital data collections.
Results:
Of 532,507 people who died, 354 (0.07%) died by suicide, including 81 receiving a home care package (0.17% of all home care package deaths), 129 in PRAC (0.03% of all deaths in PRAC), and 144 approved for but awaiting care (0.23% of all deaths while awaiting care). Factors associated with death by suicide compared to death by another cause were male sex, having a mental health condition, not having dementia, less frailty, and a hospitalization for self-injury in the year before death. Among those who were awaiting care, being born outside Australia, living alone, and not having a carer were associated with death by suicide. Those who died by suicide more often accessed Government-subsidized mental health services in the year before their death than those who died by another cause.
Conclusions:
Older men, those with diagnosed mental health conditions, those living alone and without an informal carer, and those hospitalized for self-injury are key targets for suicide prevention efforts.
The core principle that should guide any health professional caring for older adults and their families is that the “secret of caring for the patient is in caring for the patient” (Peabody). Practitioners must understand the most up-to-date biomedical and psychosocial aspects of aging, health, wellness, and disease, and strive to support the older adult to remain as active, functional, and engaged as possible. At the same time, practitioners must recognize and help patients and families understand when a palliative approach will be most effective at meeting their goals. The Choosing Wisely campaign launched by the American Board of Internal Medicine provides targeted guidance to clinicians to provide care that is effective and efficient, consistent with the essential principles. Also, the 4Ms (what Matters, Medication, Mentation, and Mobility) proposed by The John A. Hartford Foundation and Institute for Healthcare Improvement provide a framework for an Age-Friendly Health System through which practitioners can deliver optimal care for older adults.
This eighth edition of Dr Reichel's formative text remains the go-to guide for practicing physicians and allied health staff confronted with the unique problems of an increasing elderly population. Fully updated and revised, it provides a practical guide for all health specialists, emphasizing the clinical management of the elderly patient with simple to complex problems. Featuring four new chapters and the incorporation of geriatric emergency medicine into chapters. The book begins with a general approach to the management of older adults, followed by a review of common geriatric syndromes, and proceeding to an organ-based review of care. The final section addresses principles of care, including care in special situations, psychosocial aspects of our aging society, and organization of care. Particular emphasis is placed on cost-effective, patient-centered care, including a discussion of the Choosing Wisely campaign. A must-read for all practitioners seeking practical and relevant information in a comprehensive format.
To understand motivators, facilitators and challenges to dietary change amongst a diverse sample of New Zealanders with prediabetes participating in a primary care nurse-led individualised dietary intervention.
Design:
A qualitative study involving semi-structured, face-to-face interviews with a stratified sample of adults with prediabetes and BMI ≥ 25 kg/m2, purposefully selected from a larger 2-year primary care-based prediabetes dietary intervention study. Thematic analysis was undertaken. A socio-ecological model guided interpretation.
Setting:
Hawke’s Bay, Aotearoa/New Zealand, April 2018–March 2020.
Participants:
Fifty-eight people aged 28–69 years, with similar numbers of men and women, indigenous Māori and non-Māori, and those who had and had not regressed to normoglycaemia at 6 months.
Results:
Motivators for wanting to make dietary changes were determination not to progress to diabetes; wanting to be healthy and contribute to others and encouragement by others. Facilitators for adopting and maintaining changes were a strong desire to be healthy; personal determination and feeling supported. Challenges were compromised control over life and environmental factors; feeling unsupported by others; social occasions; financial constraints and living with other health conditions. Developing their own strategies to overcome challenges was empowering, enabling a sense of control. These factors were similar across demographic and glycaemic outcome groups.
Conclusions:
Influences on dietary change involved personal, interpersonal, organisational, environmental and policy factors. Although findings appeared similar across groups, dietary interventions need to address the specific ways motivators, facilitators and challenges manifest for individuals and social groups and be tailored accordingly within the context of the wider obesogenic and socio-economic environment.
The Comprehensive Assessment of Neurodegeneration and Dementia (COMPASS-ND) cohort study of the Canadian Consortium on Neurodegeneration in Aging (CCNA) is a national initiative to catalyze research on dementia, set up to support the research agendas of CCNA teams. This cross-country longitudinal cohort of 2310 deeply phenotyped subjects with various forms of dementia and mild memory loss or concerns, along with cognitively intact elderly subjects, will test hypotheses generated by these teams.
Methods:
The COMPASS-ND protocol, initial grant proposal for funding, fifth semi-annual CCNA Progress Report submitted to the Canadian Institutes of Health Research December 2017, and other documents supplemented by modifications made and lessons learned after implementation were used by the authors to create the description of the study provided here.
Results:
The CCNA COMPASS-ND cohort includes participants from across Canada with various cognitive conditions associated with or at risk of neurodegenerative diseases. They will undergo a wide range of experimental, clinical, imaging, and genetic investigation to specifically address the causes, diagnosis, treatment, and prevention of these conditions in the aging population. Data derived from clinical and cognitive assessments, biospecimens, brain imaging, genetics, and brain donations will be used to test hypotheses generated by CCNA research teams and other Canadian researchers. The study is the most comprehensive and ambitious Canadian study of dementia. Initial data posting occurred in 2018, with the full cohort to be accrued by 2020.
Conclusion:
Availability of data from the COMPASS-ND study will provide a major stimulus for dementia research in Canada in the coming years.
Shiga toxin-producing Escherichia coli (STEC) infection can cause serious illness including haemolytic uraemic syndrome. The role of socio-economic status (SES) in differential clinical presentation and exposure to potential risk factors amongst STEC cases has not previously been reported in England. We conducted an observational study using a dataset of all STEC cases identified in England, 2010–2015. Odds ratios for clinical characteristics of cases and foodborne, waterborne and environmental risk factors were estimated using logistic regression, stratified by SES, adjusting for baseline demographic factors. Incidence was higher in the highest SES group compared to the lowest (RR 1.54, 95% CI 1.19–2.00). Odds of Accident and Emergency attendance (OR 1.35, 95% CI 1.10–1.75) and hospitalisation (OR 1.71, 95% CI 1.36–2.15) because of illness were higher in the most disadvantaged compared to the least, suggesting potential lower ascertainment of milder cases or delayed care-seeking behaviour in disadvantaged groups. Advantaged individuals were significantly more likely to report salad/fruit/vegetable/herb consumption (OR 1.59, 95% CI 1.16–2.17), non-UK or UK travel (OR 1.76, 95% CI 1.40–2.27; OR 1.85, 95% CI 1.35–2.56) and environmental exposures (walking in a paddock, OR 1.82, 95% CI 1.22–2.70; soil contact, OR 1.52, 95% CI 2.13–1.09) suggesting other unmeasured risks, such as person-to-person transmission, could be more important in the most disadvantaged group.
Improving access to tuberculosis (TB) care and ensuring early diagnosis are two major aims of the WHO End TB strategy and the Collaborative TB Strategy for England. This study describes risk factors associated with diagnostic delay among TB cases in England. We conducted a retrospective cohort study of TB cases notified to the Enhanced TB Surveillance System in England between 2012 and 2015. Diagnostic delay was defined as more than 4 months between symptom onset and treatment start date. Multivariable logistic regression was used to identify demographic and clinical factors associated with diagnostic delay. Between 2012 and 2015, 22 422 TB cases were notified in England and included in the study. A third (7612) of TB cases had a diagnostic delay of more than 4 months. Being female, aged 45 years and older, residing outside of London and having extra-pulmonary TB disease were significantly associated with a diagnostic delay in the multivariable model (aOR = 1.2, 1.2, 1.2, 1.3, 1.8, respectively). This study identifies demographic and clinical factors associated with diagnostic delay, which will inform targeted interventions to improve access to care and early diagnosis among these groups, with the ultimate aim of helping reduce transmission and improve treatment outcomes for TB cases in England.
Recreational saltwater anglers from the mid-Atlantic through the Gulf of Mexico commonly target red drum. Due to concerns about overharvesting within South Carolina coupled with regional management actions, South Carolina explored the technical feasibility of stocking hatchery-produced juvenile red drum as a technique to augment the abundance of South Carolina stock. In order to assess a continued program, in 2005 a mail survey was used to collect data for estimating the economic benefits with the contingent valuation method. The theoretical validity of willingness to pay was assessed by comparison to the value of a change in red drum fishing trips that would result from the program. Benefits were compared to estimated, explicit stocking costs. We illustrate how a certainty recode approach can be used in sensitivity analysis. The net present values (NPVs) for the stocking program are positive suggesting that the program would have been economically efficient relative to no program.