The impact of participation restrictions on everyday life in long-term colorectal cancer survivors in the EnCoRe study:A mixed-method study

Purpose: Knowledge about long-term colorectal cancer (CRC) or treatment related health and functioning problems and on its impact on participation of CRC survivors in domestic life and in society is limited. We aimed to explore the nature and impact of cancer (treatment) related participation restrictions on everyday life of long-term CRC survivors, their current satisfaction with participation, and associations of health and functioning problems with participation satisfaction, using the International Classification of Functioning, Disability and Health (ICF) to comprehensively study participation.Method: Mixed-method study in 2-10 years post-diagnosis stage I-III CRC survivors (n = 151) from the cross-sectional part of the EnCoRe study. Participation restrictions were explored by semi-structured interviews in a subsample reporting participation restrictions (n = 10). Role functioning (SF36-Health Survey), fatigue (Checklist Individual Strength), and peripheral neuropathy symptoms (EORTC QLQ-CIPN20) were assessed in all participants and associations with self-reported participation satisfaction were analyzed by multivariable logistic regression models.Results: 19% of CRC survivors reported dissatisfaction with participation. Participation restrictions were reported for interpersonal relationships, work/employment, and social/civic life. CRC survivors reporting better physical and emotional role functioning were significantly less likely to be dissatisfied with their participation, whereas survivors reporting higher levels of fatigue or more peripheral neuropathy symptoms were more likely to be dissatisfied with participation.Conclusions: Colorectal cancer (treatment) related health and functioning problems negatively impacts the ability of nearly 1 in 5 long-term CRC survivors to participate in everyday life situations and their satisfaction with participation. Follow-up care needs to be able to identify and address these problems.</p

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

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