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

Prognostic models of abdominal wound dehiscence after laparotomy

Background. Portions of the prospective, multi-institutional National Veterans Affairs Surgical Quality Improvement Program were used to develop and validate a perioperative risk index to predict abdominal wound dehiscence after laparotomy. Methods. Perioperative data from 17,044 laparotomies resulting in 587 (3.4%) wound dehiscences performed at 132 Veterans Affairs Medical Centers between October 1, 1996, and September 30, 1998, were used to develop the model. Data from 17,763 laparotomies performed between October 1, 1998, and September 30, 2000, resulting in 562 (3.2%) dehiscences were used to validate the model. Models were developed using multivariable stepwise logistic regression with preoperative, intraoperative, and postoperative variables entered sequentially as independent predictors of wound dehiscence. The model was used to create a scoring system, designated the abdominal wound dehiscence risk index. Results. Factors contributing significantly to the model and their point values (in parentheses) for the risk index include CVA with no residual deficit (4), history of COPD (4), current pneumonia (4), emergency procedure (6), operative time greater than 2.5 h (2), PGY 4 level resident as surgeon (3), clean wound classification (-3), superficial (5), or deep (17) wound infection, failure to wean from the ventilator (6), one or more complications other than dehiscence (7), and return to OR during admission (-11). Scores of 11-14 are predictive of 5% risk of dehiscence while scores of \u3e14 predict 10% risk. Conclusions. This abdominal wound dehiscence risk index identifies patients at risk for dehiscence and may be useful in guiding perioperative management. © 2003 Elsevier Science (USA)

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