Advance care planning is being promoted as a central component of end-of-life policies in many developed countries, but there is concern that professionals find its implementation challenging. AimTo assess the feasibility of implementing advance care planning in UK primary care. Design of studyMixed methods evaluation of a pilot educational intervention. SettingFour general practices in south-east Scotland. MethodInterviews with 20 GPs and eight community nurses before and after a practice-based workshop; this was followed by telephone interviews with nine other GPs with a special interest in palliative care from across the UK. ResultsEnd-of-life care planning for patients typically starts as an urgent response to clear evidence of a short prognosis, and aims to achieve a 'good death'. Findings suggest that there were multiple barriers to earlier planning: prognostic uncertainty; limited collaboration with secondary care; a desire to maintain hope; and resistance to any kind of 'tick-box' approach. Following the workshop, participants' knowledge and skills were enhanced but there was little evidence of more proactive planning. GPs from other parts of the UK described confusion over terminology and were concerned about the difficulties of implementing inflexible, policy-driven care. ConclusionA clear divide was found between UK policy directives and delivery of end-of-life care in the community that educational interventions targeting primary care professionals are unlikely to address. Advance care planning has the potential to promote autonomy and shared decision making about end-of-life care, but this will require a significant shift in attitudes. Keywordsadvance care planning; cancer; palliative care; primary health care. INTRODUCTIONAdvance care planning is viewed as an intrinsic component of end-of-life care programmes in many developed countries. In the US, advance directives were originally intended to allow people to record an advance refusal of invasive, life-prolonging interventions.1 The limitations of this approach resulted in a progressive move internationally towards a broader process of advance care planning that also includes discussion of personal goals, wishes, and preferences about future care. [2][3][4] Programmes in the US and Australia that have adopted a regional strategy towards educating professionals, patients, and the general public about advance care planning have had some success. 5,6 Proactive care planning is central to recent UK policies, and the expectation is that it will now be offered to all patients approaching the end of life.

The Gold Standards Framework aims to optimize primary palliative care for patients nearing the end of their lives. This paper critically reviews the impact of the Gold Standards Framework since its introduction in 2001 and indicates direction for further research and development. Literature was accessed using specific databases and by contacting subject area specialists. The resultant literature was appraised using an established framework to evaluate healthcare interventions. Fifteen documents were reviewed. The quality of evidence is constrained by methodological limitations, but consistently demonstrates that the Gold Standards Framework improves general practice processes, co-working and the quality of palliative care. However, implementation of the Gold Standards Framework is variable and the direct impact on patients and carers is not known. We conclude that the Gold Standards Framework has considerable potential to improve end-of-life care, but further work is needed to support uptake and consistency of implementation. Additional evidence about patient and carer outcomes will add to existing insights.

Patients with cancer and their carers believe that there is an important and unique role for primary care in offering continuity of care and information that is patient-centred and holistic, throughout the cancer trajectory, from first presentation. This study successfully brought patient, carer and professional perspectives to the development of a care framework for primary care.

Alzheimer's disease (AD) patients exhibit alterations in the functional connectivity between spatially segregated brain regions which may be related to both local gray matter (GM) atrophy as well as a decline in the fiber integrity of the underlying white matter tracts. Machine learning algorithms are able to automatically detect the patterns of the disease in image data, and therefore, constitute a suitable basis for automated image diagnostic systems. The question of which magnetic resonance imaging (MRI) modalities are most useful in a clinical context is as yet unresolved. We examined multimodal MRI data acquired from 28 subjects with clinically probable AD and 25 healthy controls. Specifically, we used fiber tract integrity as measured by diffusion tensor imaging (DTI), GM volume derived from structural MRI, and the graph-theoretical measures 'local clustering coefficient' and 'shortest path length' derived from resting-state functional MRI (rs-fMRI) to evaluate the utility of the three imaging methods in automated multimodal image diagnostics, to assess their individual performance, and the level of concordance between them. We ran the support vector machine (SVM) algorithm and validated the results using leave-one-out cross-validation. For the single imaging modalities, we obtained an area under the curve (AUC) of 80% for rs-fMRI, 87% for DTI, and 86% for GM volume. When it came to the multimodal SVM, we obtained an AUC of 82% using all three modalities, and 89% using only DTI measures and GM volume. Combined multimodal imaging data did not significantly improve classification accuracy compared to the best single measures alone.

Diffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer’s disease (AD). The use of DTI as a biomarker, however, depends on its applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample.

The operational framework has the potential to produce ICT and services with high clinical impact but requires substantial investment.

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Individuals with sex chromosomal anomalies are known to be at increased risk for learning problems and in some cases social or behavioral problems. Girls with an absent or structurally abnormal second sex chromosome (the Turner syndrome) have been found to have cognitive problem solving deficits and immature, inadequate social relationships. The present study attempted to link cognitive and social problems by assessing the girls' ability to process affective cues. 17 girls with karyotypes consistent with a diagnosis of Turner syndrome were compared to a group of 16 short-stature girls of comparable age, verbal intelligence scores, height, and family socioeconomic status on the Affective Discrimination Task, which required interpretation of affective intention from facial expression. The results revealed that the Turner syndrome girls were less accurate at inferring facial affect than the short-stature controls. Analyses revealed that the Turner syndrome girls performed more poorly on spatial, attentional, and short-term memory cognitive tasks and had more psychosocial problems than the short-stature controls. Ability to discriminate facial affect may be another area of cognitive weakness for girls with the Turner syndrome and may underlie the psychosocial problems found in this sample.