Dementia diagnosis in seven languages: the Addenbrooke’s Cognitive Examination-III in India

OBJECTIVE: With the rising burden of dementia globally, there is a need to harmonize dementia research across diverse populations. The Addenbrooke's Cognitive Examination-III (ACE-III) is a well-established cognitive screening tool to diagnose dementia. But there have been few efforts to standardize the use of ACE-III across cohorts speaking different languages. The present study aimed to standardize and validate ACE-III across seven Indian languages and to assess the diagnostic accuracy of the test to detect dementia and mild cognitive impairment (MCI) in the context of language heterogeneity.  METHODS: The original ACE-III was adapted to Indian languages: Hindi, Telugu, Kannada, Malayalam, Urdu, Tamil, and Indian English by a multidisciplinary expert group. The ACE-III was standardized for use across all seven languages. In total, 757 controls, 242 dementia, and 204 MCI patients were recruited across five cities in India for the validation study. Psychometric properties of adapted versions were examined and their sensitivity and specificity were established.  RESULTS: The sensitivity and specificity of ACE-III in identifying dementia ranged from 0.90 to 1, sensitivity for MCI ranged from 0.86 to 1, and specificity from 0.83 to 0.93. Education but not language was found to have an independent effect on ACE-III scores. Optimum cut-off scores were established separately for low education (≤10 years of education) and high education (>10 years of education) groups.  CONCLUSIONS: The adapted versions of ACE-III have been standardized and validated for use across seven Indian languages, with high diagnostic accuracy in identifying dementia and MCI in a linguistically diverse context

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