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

Robotic versus laparoscopic liver resection for huge (≥10 cm) liver tumors: an international multicenter propensity-score matched cohort study of 799 cases

Background: The use of laparoscopic (LLR) and robotic liver resections (RLR) has been safely performed in many institutions for liver tumours. A large scale international multicenter study would provide stronger evidence and insight into application of these techniques for huge liver tumours >_10 cm. Methods: This was a retrospective review of 971 patients who underwent LLR and RLR for huge (>_10 cm) tumors at 42 international centers between 2002-2020. Results: One hundred RLR and 699 LLR which met study criteria were included. The comparison between the 2 approaches for patients with huge tumors were performed using 1:3 propensity-score matching (PSM) (73 vs. 219). Before PSM, LLR was associated with significantly increased frequency of previous abdominal surgery, malignant pathology, liver cirrhosis and increased median blood. After PSM, RLR and LLR was associated with no significant difference in key perioperative outcomes including media operation time (242 vs. 290 min, P=0.286), transfusion rate rate (19.2% vs. 16.9%, P=0.652), median blood loss (200 vs. 300 mL, P=0.694), open conversion rate (8.2% vs. 11.0%, P=0.519), morbidity (28.8% vs. 21.9%, P=0.221), major morbidity (4.1% vs. 9.6%, P=0.152), mortality and postoperative length of stay (6 vs. 6 days, P=0.435). Conclusions: RLR and LLR can be performed safely for selected patients with huge liver tumours with excellent outcomes. There was no significant difference in perioperative outcomes after RLR or LLR