Institution
North Bristol NHS Trust
Healthcare•Bristol, United Kingdom•
About: North Bristol NHS Trust is a healthcare organization based out in Bristol, United Kingdom. It is known for research contribution in the topics: Population & Medicine. The organization has 2204 authors who have published 2811 publications receiving 61110 citations.
Papers published on a yearly basis
Papers
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TL;DR: A positive deviance case study of a high-performing UK maternity unit is reported to examine how it achieved and sustained excellent safety outcomes and enhances understanding of what makes a maternity unit safe, paving the way for better design of improvement approaches.
60 citations
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TL;DR: In a previous unpublished observation, unacceptably high bacterial counts, presumably due to shedding episodes, occurred in two of 56 (3.57%) slit-air samples during arthroplasty surgery in a laminar flow operating theatre as discussed by the authors.
60 citations
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TL;DR: An artificial intelligence based analytics framework is designed and developed using machine learning and natural language processing techniques for intelligent analysis and automated aggregation of patient information and interaction trajectories in online support groups to investigate, analyse and derive actionable insights from patient-reported information on prostate cancer.
Abstract: BACKGROUND: A primary variant of social media, online support groups (OSG) extend beyond the standard definition to incorporate a dimension of advice, support and guidance for patients. OSG are complementary, yet significant adjunct to patient journeys. Machine learning and natural language processing techniques can be applied to these large volumes of unstructured text discussions accumulated in OSG for intelligent extraction of patient-reported demographics, behaviours, decisions, treatment, side effects and expressions of emotions. New insights from the fusion and synthesis of such diverse patient-reported information, as expressed throughout the patient journey from diagnosis to treatment and recovery, can contribute towards informed decision-making on personalized healthcare delivery and the development of healthcare policy guidelines. METHODS AND FINDINGS: We have designed and developed an artificial intelligence based analytics framework using machine learning and natural language processing techniques for intelligent analysis and automated aggregation of patient information and interaction trajectories in online support groups. Alongside the social interactions aspect, patient behaviours, decisions, demographics, clinical factors, emotions, as subsequently expressed over time, are extracted and analysed. More specifically, we utilised this platform to investigate the impact of online social influences on the intimate decision scenario of selecting a treatment type, recovery after treatment, side effects and emotions expressed over time, using prostate cancer as a model. Results manifest the three major decision-making behaviours among patients, Paternalistic group, Autonomous group and Shared group. Furthermore, each group demonstrated diverse behaviours in post-decision discussions on clinical outcomes, advice and expressions of emotion during the twelve months following treatment. Over time, the transition of patients from information and emotional support seeking behaviours to providers of information and emotional support to other patients was also observed. CONCLUSIONS: Findings from this study are a rigorous indication of the expectations of social media empowered patients, their potential for individualised decision-making, clinical and emotional needs. The increasing popularity of OSG further confirms that it is timely for clinicians to consider patient voices as expressed in OSG. We have successfully demonstrated that the proposed platform can be utilised to investigate, analyse and derive actionable insights from patient-reported information on prostate cancer, in support of patient focused healthcare delivery. The platform can be extended and applied just as effectively to any other medical condition.
60 citations
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TL;DR: Treatment of complex bicondylar tibial plateau fractures with either a locking plate or a TSF gives similar clinical and radiological outcomes, despite treatment by experienced surgeons using modern surgical techniques.
Abstract: Unstable bicondylar tibial plateau fractures
are rare and there is little guidance in the literature as to the
best form of treatment. We examined the short- to medium-term outcome
of this injury in a consecutive series of patients presenting to
two trauma centres. Between December 2005 and May 2010, a total
of 55 fractures in 54 patients were treated by fixation, 34 with
peri-articular locking plates and 21 with limited access direct
internal fixation in combination with circular external fixation
using a Taylor Spatial Frame (TSF). At a minimum of one year post-operatively,
patient-reported outcome measures including the WOMAC index and
SF-36 scores showed functional deficits, although there was no significant
difference between the two forms of treatment. Despite low outcome scores,
patients were generally satisfied with the outcome. We achieved
good clinical and radiological outcomes, with low rates of complication.
In total, only three patients (5%) had collapse of the joint of
> 4 mm, and metaphysis to diaphysis angulation of greater than 5o,
and five patients (9%) with displacement of > 4 mm. All patients
in our study went on to achieve full union. This study highlights the serious nature of this injury and generally
poor patient-reported outcome measures following surgery, despite
treatment by experienced surgeons using modern surgical techniques.
Our findings suggest that treatment of complex bicondylar tibial
plateau fractures with either a locking plate or a TSF gives similar
clinical and radiological outcomes. Cite this article: Bone Joint J 2014;96-B:956–62.
60 citations
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TL;DR: Targeted gene panel testing is an unbiased approach which overcomes the limitations imposed by limited existing knowledge for rare genes, reveals high heterogeneity, and provides high diagnostic yield and is therefore a highly efficient and cost effective tool for achieving a genetic diagnosis for IPN.
Abstract: Inherited peripheral neuropathy (IPN) is a clinically and genetically heterogeneous group of disorders with more than 90 genes associated with the different subtypes. Sequential gene screening is gradually being replaced by next generation sequencing (NGS) applications. We designed and validated a targeted NGS panel assay including 56 genes associated with known causes of IPN. We report our findings following NGS panel testing of 448 patients with different types of clinically-suspected IPN. Genetic diagnosis was achieved in 137 patients (31 %) and involved 195 pathogenic variants in 31 genes. 93 patients had pathogenic variants in genes where a resulting phenotype follows dominant inheritance, 32 in genes where this would follow recessive inheritance, and 12 presented with X-linked disease. Almost half of the diagnosed patients (64) had a pathogenic variant either in genes not previously available for routine diagnostic testing in a UK laboratory (50 patients) or in genes whose primary clinical association was not IPN (14). Seven patients had a pathogenic variant in a gene not hitherto indicated from their phenotype and three patients had more than one pathogenic variant, explaining their complex phenotype and providing information essential for accurate prediction of recurrence risks. Our results demonstrate that targeted gene panel testing is an unbiased approach which overcomes the limitations imposed by limited existing knowledge for rare genes, reveals high heterogeneity, and provides high diagnostic yield. It is therefore a highly efficient and cost effective tool for achieving a genetic diagnosis for IPN.
60 citations
Authors
Showing all 2226 results
Name | H-index | Papers | Citations |
---|---|---|---|
Debbie A Lawlor | 147 | 1114 | 101123 |
Stephen T. Holgate | 142 | 870 | 82345 |
Paul Jackson | 141 | 1372 | 93464 |
E. Thomson | 103 | 992 | 51777 |
Paul Abrams | 91 | 505 | 51539 |
Susan M. Ring | 91 | 268 | 45339 |
Richard Baker | 83 | 514 | 22970 |
Seth Love | 74 | 344 | 30535 |
Kenneth R Fox | 70 | 269 | 19099 |
Evan L. Flatow | 70 | 245 | 15692 |
Paul Roderick | 67 | 392 | 20741 |
Robert J. Hinchliffe | 66 | 298 | 14818 |
Tim Cook | 61 | 340 | 14170 |
Jasmeet Soar | 57 | 252 | 20311 |
Salomone Di Saverio | 55 | 338 | 9123 |