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Factors Related to Second Arm or leg Function in Cancers of the breast Survivors.
Recent detections of large gatherings of Whooping Cranes suggest that flock sizes may be increasing at some stopover locations during both the spring and fall migrations. We used the public sightings database managed by the US Fish and Wildlife Service from 1942 to 2018 to analyze data for long-term trends in group size. We then examined the spatial distribution of large groups to explore potential explanations for these occurrences. The proportion of Whooping Crane groups comprised of 2, 3, and 4-6 individuals showed no trend over time. However, observations of individuals showed a declining trend and groups of 7-9 and ≥10 showed an increasing trend. The frequency of groups observed exceeding 5 and 10 individuals were better predicted by survey year than by Whooping Crane population size suggesting that an increasing population is not the sole driver of large group occurrences. Our results indicate that large groups occur disproportionately within the 50% migration corridor, at staging areas within the first or last 20-30% of the migration path, and near conservation-managed wetlands, particularly within the southern Great Plains. Our results suggest that in addition to population growth, conspecific attraction, location within the migration corridor, and habitat loss may be contributing to large group occurrences. Further research is needed to determine the degree to which these factors influence large Whooping Crane group formation. The gathering of large numbers of Whooping Cranes in a single location presents potential tradeoffs for the species. While increasing group sizes may improve threat detection and avoidance, it comes at a cost of increased disease and mass mortality risk. © 2020 The Authors.Background and Purpose Radiation esophagitis is a clinically important toxicity seen with treatment for locally-advanced non-small cell lung cancer. There is considerable disagreement among prior studies in identifying predictors of radiation esophagitis. We apply machine learning algorithms to identify factors contributing to the development of radiation esophagitis to uncover previously unidentified criteria and more robust dosimetric factors. Materials and Methods We used machine learning approaches to identify predictors of grade ≥ 3 radiation esophagitis in a cohort of 202 consecutive locally-advanced non-small cell lung cancer patients treated with definitive chemoradiation from 2008 to 2016. We evaluated 35 clinical features per patient grouped into risk factors, comorbidities, imaging, stage, histology, radiotherapy, chemotherapy and dosimetry. Univariate and multivariate analyses were performed using a panel of 11 machine learning algorithms combined with predictive power assessments. Results All patients were treated to a median dose of 66.6 Gy at 1.8 Gy per fraction using photon (89.6%) and proton (10.4%) beam therapy, most often with concurrent chemotherapy (86.6%). 11.4% of patients developed grade ≥ 3 radiation esophagitis. selleck inhibitor On univariate analysis, no individual feature was found to predict radiation esophagitis (AUC range 0.45-0.55, p ≥ 0.07). In multivariate analysis, all machine learning algorithms exhibited poor predictive performance (AUC range 0.46-0.56, p ≥ 0.07). Conclusions Contemporary machine learning algorithms applied to our modern, relatively large institutional cohort could not identify any reliable predictors of grade ≥ 3 radiation esophagitis. Additional patients are needed, and novel patient-specific and treatment characteristics should be investigated to develop clinically meaningful methods to mitigate this survival altering toxicity. © 2020 The Author(s).As the global COVID-19 pandemic escalates there is a need within radiation oncology to work to support our patients in the best way possible. Measures are required to reduce infection spread between patients and within the workforce. Departments need contingency planning to create capacity and continue essential treatments despite a reduced workforce. The #radonc community held an urgent online journal club on Twitter in March 2020 to discuss these issues and create some consensus on crucial next steps. There were 121 global contributors. This document summarises these discussions around themes of infection prevention, rationalisation of workload and working practice in the presence of infection. © 2020 The Authors.Background and Objectives Older people are likely to transition to a new home closer to family who can provide assistance or to long-term residential care as their health declines and their care needs increase. A minority choose to move to "age-friendly" housing before the onset of disability, but the majority prefer to "age in place" and defer moving until health crises compel a transition. Older people living with dementia are likely to move into residential care, but not much is known about the role they play in decision making around these moves. This qualitative study addresses this gap in knowledge by examining how a rare cohort of "older old" people, most with some level of cognitive impairment, were involved in decisions surrounding assistance seeking and moving to a care home. Research Design and Methods Thematic analysis of qualitative interview data from Cambridge City over-75s Cohort (CC75C) study participants aged 95 years and older, who had moved in later life, and their proxy informants (n = 26). Results Moves at such an old age were made due to a complexity of push and pull factors which had layered dynamics of decision making. In most cases (n = 22), decision making involved other people with varying degrees of decision ownership. Only four older people, who moved voluntarily, had full ownership of the decision to move. Many relatives reported being traumatized by events leading up to the move. Discussion and Implications "Older old" people are sometimes unable to make their own decisions about moving due to the urgency of health crisis and cognitive decline. There is a need to support relatives to discuss moving and housing options at timely junctures before health crises intervene in an effort to optimize older people's participation in decision making. © The Author(s) 2019. Published by Oxford University Press on behalf of The Gerontological Society of America.
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