| 1. |
- La Russa, Mariaclelia, et al.
(författare)
-
Assessment of palliative care training in gynaecological oncology : a survey among European Network of Young Gynae-Oncologists (ENYGO) members
- 2020
-
Ingår i: BMJ Supportive & Palliative Care. - : BMJ Publishing Group Ltd. - 2045-435X .- 2045-4368.
-
Tidskriftsartikel (refereegranskat)abstract
- INTRODUCTION: Palliative care is an important aspect of gynaecological oncology practice. In order to successfully integrate end-of-life (EOL) care in the disease trajectory, it is crucial to incorporate systematic training in subspecialty programmes in gynaecological oncology. We aimed to evaluate the quality of training in palliative care across gynaecological oncology fellows in Europe and to provide a framework to facilitate learning opportunities.METHODS: A web-based questionnaire was sent to members of the European Network of Young Gynae-Oncologists (ENYGO). The survey consisted of 36 items covering six domains: respondents' characteristics, quality and quantity of teaching, curriculum achievements, observation and feedback, EOL clinical practice and attitudes about palliative care.RESULTS: Of the 703 clinicians enrolled in the study, 142 responded (20.2%). Although the majority worked in university hospitals, only half of them (47%) were in a formal subspecialty programme. The majority of respondents (60%) were trained without a mandatory rotation in palliative care units and considered the quality of EOL care teaching as 'very poor' or 'poor' (57.7%). The majority of respondents (71.6%) did not receive any supervision or feedback at the time of their first consultation on changing the goals of care.CONCLUSION: Our study underlines lack of structured teaching and supervision in palliative care contents among European fellows in gynaecological oncology. Broad education of healthcare providers is a key factor to achieve the integration of palliative care in gynaecological oncology practice. Stakeholders like European Society of Gynaecological Oncology/ENYGO play an important role to facilitate educational activities and training programmes targeting to EOL care.
|
|
| 2. |
- Lindemann, K., et al.
(författare)
-
Elderly gynaecological cancer patients at risk for poor end of life care : a population-based study from the Swedish Register of Palliative Care
- 2020
-
Ingår i: Acta Oncologica. - : Taylor & Francis. - 0284-186X .- 1651-226X. ; 59:6, s. 636-643
-
Tidskriftsartikel (refereegranskat)abstract
- Introduction: Poorer end-of-life (EOL) care for elderly cancer patients has been reported. We assessed the impact of age on 13 indicators for the quality of EOL care as well as adherence to 6 national quality indicators in gynaecological cancer patients.Methods: Age-dependent differences in 13 palliative care quality indicators were studied in gynaecological cancer patients registered in the population-based Swedish Register of Palliative Care. Association between the patient's age and each quality indicator was analyzed by logistic regression, adjusted for place of death where appropriate. Adherence to six national quality indicators determined by the Swedish National Board of Health and Welfare was estimated in all patients.Results: We included 3940 patients with the following age distribution: 1.6% were 18-39 years of age, 12.3% 40-59 years, 37.2% 60-74 years, 28.9% 75-84 years and 20% were ≥85 years. Age-dependent differences in implementation rate were present for some of the 13 quality indicators. Compared to elderly cancer patients, younger patients were more likely to be cared for by a specialized palliative care service, more often informed about imminent death as well as assessed for pain. For most national quality indicators, the goal level was not met. Only for the 'on demand prescription for pain', the goal level was reached.Conclusions: EOL care did not meet national quality indicators in this population-based data from Sweden, in particular in the elderly population. Elderly gynaecological cancer patients are at high risk of poorer EOL care without the involvement of specialized palliative care services. Palliative care services need to be implemented across all institutions of EOL care to ensure good and equal care.
|
|
| 3. |
- Mejia, A. F., et al.
(författare)
-
A Bayesian General Linear Modeling Approach to Cortical Surface fMRI Data Analysis
- 2020
-
Ingår i: Journal of the American Statistical Association. - : Informa UK Limited. - 0162-1459 .- 1537-274X.
-
Tidskriftsartikel (refereegranskat)abstract
- Cortical surface functional magnetic resonance imaging (cs-fMRI) has recently grown in popularity versus traditional volumetric fMRI. In addition to offering better whole-brain visualization, dimension reduction, removal of extraneous tissue types, and improved alignment of cortical areas across subjects, it is also more compatible with common assumptions of Bayesian spatial models. However, as no spatial Bayesian model has been proposed for cs-fMRI data, most analyses continue to employ the classical general linear model (GLM), a "massive univariate" approach. Here, we propose a spatial Bayesian GLM for cs-fMRI, which employs a class of sophisticated spatial processes to model latent activation fields. We make several advances compared with existing spatial Bayesian models for volumetric fMRI. First, we use integrated nested Laplacian approximations, a highly accurate and efficient Bayesian computation technique, rather than variational Bayes. To identify regions of activation, we utilize an excursions set method based on the joint posterior distribution of the latent fields, rather than the marginal distribution at each location. Finally, we propose the first multi-subject spatial Bayesian modeling approach, which addresses a major gap in the existing literature. The methods are very computationally advantageous and are validated through simulation studies and two task fMRI studies from the Human Connectome Project. for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
|
|
