Interest and barriers to research in obstetric haematology – findings from a national survey in the United Kingdom

Introduction: In 2021, the steering committee members of British Society of Haematology Obstetric Haematology Special Interest Group noted difficulties in opening research studies. This led to the development of a survey to further evaluate this issue. Method: An electronic survey was distributed to all members of the British Society of Haematology Obstetric Haematology Special Interest Group and to relevant specialty leads of the National Institute for Health and Care Research Clinical Research Network for further dissemination within these networks. Results: Responses were received from 65 participants (73% consultant grade); mainly haematologists (52%) or obstetricians (39%). Less than a third of participants reported dedicated time for research in their job plan, with only five participants reporting no challenges in opening research studies in obstetric haematology. Discussion: The survey confirmed significant interest in obstetric haematology research, with barriers to participation. We propose further actions to facilitate increased research

Omnidirectional Transfer for Quasilinear Lifelong Learning

In biological learning, data are used to improve performance not only on the current task, but also on previously encountered and as yet unencountered tasks. In contrast, classical machine learning starts from a blank slate, or tabula rasa, using data only for the single task at hand. While typical transfer learning algorithms can improve performance on future tasks, their performance on prior tasks degrades upon learning new tasks (called catastrophic forgetting). Many recent approaches for continual or lifelong learning have attempted to maintain performance given new tasks. But striving to avoid forgetting sets the goal unnecessarily low: the goal of lifelong learning, whether biological or artificial, should be to improve performance on all tasks (including past and future) with any new data. We propose omnidirectional transfer learning algorithms, which includes two special cases of interest: decision forests and deep networks. Our key insight is the development of the omni-voter layer, which ensembles representations learned independently on all tasks to jointly decide how to proceed on any given new data point, thereby improving performance on both past and future tasks. Our algorithms demonstrate omnidirectional transfer in a variety of simulated and real data scenarios, including tabular data, image data, spoken data, and adversarial tasks. Moreover, they do so with quasilinear space and time complexity

Docking topical hierarchies:A comparison of two algorithms for reconciling keyword structures

Hierarchies are a natural way for people to organize information, as reflected by the common use of “broader/narrower ” term relation in keyword thesauri. However, different people and organizations tend to construct different conceptual hierarchies (e.g., contrast Yahoo! with the UseNet news hierarchy), and while there are often significant commonalties it is in general quite difficult to fully reconcile them. We are particularly interested in the problem of “docking ” a narrower, more focused and refined topical hierarchy into a broader one, and describe two algorithms for accomplishing this task. The first matches hierarchies based on a bipartite matching algorithm of (textual) features of nodes without consideration of their hierarchic organization, and the second is based on an attributed tree matching algorithm which uses both hierarchic structure and node features. We present experimental results showing the performance of both algorithms on a set of very different topical hierarchies, all designed to represent the field of Computer Science. These show that hierarchic structure does indeed allow more accurate matches than nodes alone.

Recent update on crosslinked polyethylene in total hip arthroplasty

More than two decades after their clinical introduction, crosslinked polyethylenes (XLPE) have been widely adopted. Though concerns were initially raised regarding oxidation and brittleness, on a large scale, the first generation of XLPE continues to be highly effective 15 years after the surgery, even in a young and active population. Remelted XLPE might display lower wear rates than annealed XLPE. Second generation XLPEs, not only including sequentially irradiated and annealed but also associated with antioxidants, demonstrate encouraging short- to mid-term results. Registry data support clinical trial reports. Even in less favorable settings (lipped liners, dual mobility cups, revision surgery, hip resurfacing) results are promising. However, failures (fractures) have already been described. Therefore, a high level of surveillance remains crucial