44 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

    Recruitment and baseline characteristics of young adults at risk of early-onset knee osteoarthritis after ACL reconstruction in the SUPER-Knee trial

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    Objectives:The study aims to (1) report the process of recruiting young adults into a secondary knee osteoarthritis prevention randomised controlled trial (RCT) after anterior cruciate ligament reconstruction (ACLR); (2) determine the number of individuals needed to be screened to include one participant (NNS) and (3) report baseline characteristics of randomised participants. Methods:The SUpervised exercise-therapy and Patient Education Rehabilitation (SUPER)-Knee RCT compares SUPER and minimal intervention for young adults (aged 18-40 years) with ongoing symptoms (ie, mean score of &lt;80/100 from four Knee injury and Osteoarthritis Outcome Score subscales (KOOS4)) 9-36 months post-ACLR. The NNS was calculated as the number of prospective participants screened to enrol one person. At baseline, participants provided medical history, completed questionnaires (demographic, injury/surgery, rehabilitation characteristics) and underwent physical examination. Results:1044 individuals were screened to identify 567 eligible people, from which 184 participants (63% male) enrolled. The sample of enrolled participants was multicultural (29% born outside Australia; 2% Indigenous Australians). The NNS was 5.7. For randomised participants, mean±SD age was 30±6 years. The mean body mass index was 27.3±5.2 kg/m2, with overweight (43%) and obesity (21%) common. Participants were, on average, 2.3 years post-ACLR. Over half completed &lt;8 months of postoperative rehabilitation, with 56% having concurrent injury/surgery to meniscus and/or cartilage. The most affected KOOS (0=worst, 100=best) subscale was quality of life (mean 43.7±19.1). Conclusion:Young adults post-ACLR were willing to participate in a secondary osteoarthritis prevention trial. Sample size calculations should be multiplied by at least 5.7 to provide an estimate of the NNS. The SUPER-Knee cohort is ideally positioned to monitor and intervene in the early development and trajectory of osteoarthritis.</p
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