Background

Patient-Reported Expanded Disability Status Scale (PREDSS) tools are an attractive alternative to the Expanded Disability Status Scale (EDSS) during long term or geographically challenging studies, or in pressured clinical service environments.

Objectives

Because the studies reporting these tools have used different metrics to compare the PREDSS and EDSS, we undertook an individual patient data level analysis of all available tools.

Methods

Spearman's rho and the Bland-Altman method were used to assess correlation and agreement respectively.

Results

A systematic search for validated PREDSS tools covering the full EDSS range identified eight such tools. Individual patient data were available for five PREDSS tools. Excellent correlation was observed between EDSS and PREDSS with all tools. A higher level of agreement was observed with increasing levels of disability. In all tools, the 95% limits of agreement were greater than the minimum EDSS difference considered to be clinically significant. However, the intra-class coefficient was greater than that reported for EDSS raters of mixed seniority. The visual functional system was identified as the most significant predictor of the PREDSS-EDSS difference.

Conclusion

This analysis will (1) enable researchers and service providers to make an informed choice of PREDSS tool, depending on their individual requirements, and (2) facilitate improvement of current PREDSS tools.

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.