Multiagency approaches to preventing sudden unexpected death in infancy (SUDI): a review and analysis of UK policies

Background Recent reviews of sudden unexpected deaths in infancy (SUDI) in England recommend a multiagency working (MAW) approach to prevention but lack clear guidance around how this might be implemented.Aims In England, local authorities commission and oversee public health services. This review examines how local authority policies address implementation of MAW for SUDI prevention to understand local variations and identify strengths and weaknesses.Methods Using a comprehensive list of all metropolitan, county, unitary councils and London boroughs in England, we systematically searched local authority websites for relevant published documents and submitted freedom of information (FOI) requests where policies or guidance for SUDI prevention had not been sourced online. We extracted data from documents using a standardised form to summarise policy contents which were then collated, described and appraised.Findings We searched the websites of 152 council and London boroughs, identifying 36 relevant policies and guidelines for staff. We submitted 116 FOI requests which yielded 64 responses including six valid documents: 45% (52/116) of local authorities did not respond. Seventeen councils shared the same guidance under safeguarding partnerships; removal of duplicates resulted in 26 unique documents. Only 15% (4/26) of the documents included a detailed plan for how MAW approaches were to be implemented despite 73% (19/26) of the documents mentioning the importance of engaging the MAW in raising awareness of safe sleep for babies with vulnerable families. Five areas of variation were identified across policies: (1) scope, (2) responsibilities, (3) training, (4) implementation and (5) evaluation.Conclusions There are discrepancies between local authorities in England in whether and how MAW for SUDI prevention is carried out. Strengths and weaknesses of approaches are identified to inform future development of MAW for SUDI prevention

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe