The role of cranial and thoracic EMG within diagnostic criteria for ALS.

INTRODUCTION: The contribution of cranial and thoracic region electromyography (EMG) to diagnostic criteria for amyotrophic lateral sclerosis (ALS) has not been evaluated. METHODS: Clinical and EMG data from each craniospinal region were retrospectively assessed in 470 patients; 214 had ALS. Changes to diagnostic classification in Awaji-Shima and revised El Escorial criteria following withdrawal of cranial/thoracic EMG data were ascertained. RESULTS: Sensitivity for lower motor neuron involvement in ALS was highest in cervical/lumbar regions; specificity was highest in cranial/thoracic regions. Cranial EMG contributed to definite/probable Awaji-Shima categorization in 1.4% of patients. Thoracic EMG made no contribution. For revised El Escorial criteria, cranial and thoracic data reclassified 1% and 5% of patients, respectively. CONCLUSION: Cranial EMG data make small contributions to both criteria, thoracic data contribute only to the revised El Escorial criteria. However, cranial and thoracic region abnormalities are specific in ALS. Consideration should be given to allowing greater diagnostic contribution from thoracic EMG

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 science. © The Author(s) 2019. Published by Oxford University Press