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

Similar mortality with general or regional anesthesia in elderly hip fracture patients

Background and purpose — There is continuing confusion among practitioners with regard to the optimal choice of anesthetic type for repair of hip fractures. We investigated whether type of anesthetic was associated with short-term mortality after hip fracture surgery. Patients and methods — We conducted a retrospective cohort study of patients with surgically treated hip fractures, performed between January 1, 2009 and December 31, 2012. Exposure of interest was anesthesia type (general, spinal/neuroaxial, and mixed). Endpoints were 30-, 90-, and 365-day post-surgery mortality. Multivariable conditional logistic regression models were used and odds ratios (ORs) and 95% confidence intervals (CIs) are reported. Results — Of the 7,585 participants, 5,412 (71%) were women and the median age was 80 (IQR: 72–85) years old. Of the total cohort, 4,257 (56%) received general anesthesia, 3,059 (40%) received spinal/neuroaxial, and 269 (4%) received mixed anesthesia. Overall, the incidence of 30-, 90-, and 365-day mortality was 4% (n = 307), 8% (n = 583), and 15% (n = 1,126), respectively. When compared with general anesthesia, the 365-day odds of mortality was marginally lower in patients with spinal/neuroaxial anesthesia (OR = 0.84, CI: 0.70–1.0), but it was similar in patients with mixed anesthesia (OR = 1.3, CI: 0.70–2.3). No other statistically significant differences were observed. Interpretation — Regarding mortality, this study does not support specific recommendations regarding the type of anesthetic in surgery of fractured hips

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