Status Epilepticus Severity Score (STESS): a tool to orient early treatment strategy.

Status epilepticus (SE) treatment ranges from small benzodiazepine doses to coma induction. For some SE subgroups, it is unclear how the risk of an aggressive therapeutic approach balances with outcome improvement. We recently developed a prognostic score (Status Epilepticus Severity Score, STESS), relying on four outcome predictors (age, history of seizures, seizure type and extent of consciousness impairment), determined before treatment institution. Our aim was to assess whether the score might have a role in the treatment strategy choice. This cohort study involved adult patients in three centers. For each patient, the STESS was calculated before primary outcome assessment: survival vs. death at discharge. Its ability to predict survival was estimated through the negative predictive value for mortality (NPV). Stratified odds ratios (OR) for mortality were calculated considering coma induction as exposure; strata were defined by the STESS level. In the observed 154 patients, the STESS had an excellent negative predictive value (0.97). A favorable STESS was highly related to survival (P < 0.001), and to return to baseline clinical condition in survivors (P < 0.001). The combined Mantel-Haenszel OR for mortality in patients stratified after coma induction and their STESS was 1.5 (95 % CI: 0.59-3.83). The STESS reliably identifies SE patients who will survive. Early aggressive treatment could not be routinely warranted in patients with a favorable STESS, who will almost certainly survive their SE episode. A randomized trial using this score would be needed to confirm this hypothesis

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