Quiet eye training facilitates visuomotor coordination in children with developmental coordination disorder.

INTRODUCTION: Quiet eye training (QET) has been shown to be more effective than traditional training (TT) methods for teaching a throw and catch task to typically developing 8-10 yr old children. The current study aimed to apply the technique to children with developmental coordination disorder (DCD). METHOD: 30 children with DCD were randomly allocated into TT or QET intervention groups. The TT group were taught how to control their arm movements during the throw and catch phases, while the QET group were also taught to fixate a target location on the wall prior to the throw (quiet eye1; QE1), followed by tracking the ball prior to the catch (quiet eye2; QE2). Performance, gaze and motion analysis data were collected at pre/post-training and 6-week retention. RESULTS: The QET group significantly increased QE durations from pre-training to delayed retention (QE1 = +247 ms, QE2 = +19%) whereas the TT group experienced a reduction (QE1 = -74 ms, QE2 = -4%). QET participants showed significant improvement in the quality of their catch attempts and increased elbow flexion at catch compared to the TT group (QET = -28°, TT = -1°). CONCLUSION: QET changed DCD children's ability to focus on a target on the wall prior to the throw, followed by better anticipation and pursuit tracking on the ball, which in turn led to improved catching technique. QET may be an effective adjunct to traditional instructions, for therapists teaching visuomotor skills to children with DCD

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