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

Trigeminal neuralgia. Pathophysiology and treatment.

Trigeminal neuralgia is a very peculiar disease. The pain, also known as "tic douloureux", is paroxystic and very severe. It can be triggered by a light cutaneous stimulus on a very localized spot on the face (the so-called "trigger zone"). The patient can sometimes benefit from long remissions without any treatment. With the exception of multiple sclerosis and of uncommon cases of posterior fossa tumours or other lesions impinging on the trigeminal nerve, ganglion or root, trigeminal neuralgia is considered as "idiopathic". Some benign abnormality had for long been suspected. The current opinion is now in favour of a "neurovascular conflict": an artery, most often a loop of the superior or anteroinferior cerebellar artery, has an offending contact with the trigeminal nerve root, which results in localized demyelination and ectopic triggering of neuronal discharges. This hypothesis is in agreement with the relief provided by antiepileptic drugs and is supported by recent neuroimaging data. Therapeutic options are reviewed: very efficient drugs are available but fail to provide a significant relief and/or have important side effects in many cases. Surgical alternatives are available, for which guidelines are proposed

Critical review of oral drug treatments for diabetic neuropathic pain-clinical outcomes based on efficacy and safety data from placebo-controlled and direct comparative studies.

Item does not contain fulltextThe present review aims to evaluate the efficacy and safety of a selection of oral treatments for the management of painful diabetic neuropathy. A literature review was conducted retrieving placebo-controlled and direct comparative studies with a selection of oral treatments for painful diabetic neuropathy. All studies were analyzed with regard to efficacy and tolerability. Efficacy was evaluated as the percentage improvement in pain intensity between baseline and endpoint. Tolerability was evaluated by means of study discontinuations due to adverse events and by incidence of drug-related adverse events.The analyzed trials enrolled different patient populations with mostly small numbers of patients. The great variability in dosages and dose titration schemes, cross-over designs with variable wash-out periods, and other design schemes made comparison between the different studies difficult. Gabapentin, lamotrigine, tramadol, oxycodone, mexiletine, and acetyl-L-carnitine were the only treatments studied in large (at least 100 patients), placebo-controlled parallel group trials.It is concluded that standardization in design and reporting for comparison of treatments is needed. Validated questionnaires for evaluation of the efficacy and safety should be further developed. Based on the reviewed randomised controlled trials, gabapentin shows good efficacy, a favourable side-effect profile with lack of drug interactions and therefore it may be a first choice treatment in painful diabetic neuropathy, especially in the elderly. However, head to head trials of current treatments are lacking and therefore randomized controlled trials are required to address this issue

Silent Self-Stabilizing Scheme for Spanning-Tree-like Constructions

We propose a general scheme, called Algorithm STlC, to compute spanning-tree-like data structures on arbitrary networks. STlC is self-stabilizing and silent and, despite its generality, is also efficient. It is written in the locally shared memory model with composite atomicity assuming the distributed unfair daemon, the weakest scheduling assumption of the model. Its stabilization time is in O(nmax) rounds, where nmax is the maximum number of processes in a connected component. We also exhibit polynomial upper bounds on its stabilization time in steps and process moves holding for large classes of instantiations of Algorithm STlC. We illustrate the versatility of our approach by proposing several such instantiations that efficiently solve classical problems such as leader election, as well as, unconstrained and shortest-path spanning tree constructions

Silent self-stabilizing scheme for spanning-tree-like constructions

International audienceIn this paper, we propose a general scheme, called Algorithm $\mathsf{STlC}$, to compute spanning-tree-like data structures on arbitrary networks. $\mathsf{STlC}$ is self-stabilizing and silent and, despite its generality, is also efficient. It is written in the locally shared memory model with composite atomicity assuming the distributed unfair daemon, the weakest scheduling assumption of the model.Its stabilization time is in $n_{\texttt{maxCC}}$ rounds, where $n_{\texttt{maxCC}}$ is the maximum number of processes in a connected component. We also exhibit polynomial upper bounds on its stabilization time in steps and process moves holding for large classes of instantiations of Algorithm $\mathsf{STlC}$.We illustrate the versatility of our approach by proposing several such instantiations that efficiently solve classical problems such as leader election, as well as, unconstrained and shortest-path spanning tree constructions