4 research outputs found

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

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    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

    Risk Factors for Obesity and Overfat among Primary School Children in Mashonaland West Province, Zimbabwe

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    Associated childhood obesity risk factors are not well established in developing countries such as Zimbabwe and this information is essential for tailored intervention development. This study aimed to identify prominent risk factors for overweight/obese and overfat/obese among primary school children of Mashonaland West Province in Zimbabwe. A school-based cross-sectional study was conducted using multi-stage random cluster sampling approach (30 × 30). Bivariate and multivariable logistic regression was employed and identified the risk factors for overweight/obese and overfat/obese. A total of 974 participants were enrolled in the study. Prominent significant risk factors of overweight/obese after multivariable adjustment were higher socio-economic households; parental diabetes status; and living in Makonde, Zvimba, Sanyati or Mhondoro-Ngezi district as opposed to Hurungwe district. Risk factors for overfat/obese that remained statically significant were children in urban areas (aOR = 3.19, 95% CI: 2.18−4.66, p = 0.000), being one child in a household, and parents who have diabetes mellitus. Living in Makonde, Sanyati, and Zvimba district remained associated with overfat/obese compared to Hurungwe district. This study has identified prominent proximal determinants of overweight/obese and overfat/obese among primary school children in Zimbabwe, to better assist policy guidance. Aggressive education on good nutrition activities should be tailored and targeted to most affected urban areas within high-risk districts

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

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

    No full text
    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 science. © The Author(s) 2019. Published by Oxford University Press
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