Online Training on Skin Cancer Diagnosis in Rheumatologists: Results from a Nationwide Randomized Web-Based Survey

International audiencePatients with inflammatory rheumatisms, such as rheumatoid arthritis, are more prone to develop skin cancers than the general population, with an additional increased incidence when receiving TNF blockers. There is therefore a need that physicians treating patients affected with inflammatory rheumatisms with TNF blockers recognize malignant skin lesions, requiring an urgent referral to the dermatologist and a potential withdrawal or modification of the immunomodulatory treatment. We aimed to demonstrate that an online training dedicated to skin tumors increase the abilities of rheumatologists to discriminate skin cancers from benign skin tumors. A nationwide randomized web-based survey involving 141 French rheumatologists was conducted. The baseline evaluation included short cases with skin lesion pictures and multiple choice questions assessing basic knowledge on skin cancers. For each case, rheumatologists had to indicate the nature of skin lesion (benign; premalignant/ malignant), their level of confidence in this diagnosis (10-points Likert scale), and the precise dermatological diagnosis among 5 propositions. Different scores were established. After randomization, only one group had access to the online formation consisting in 4 elearning modules on skin tumors, of 15 minutes each (online training group). After reevaluation, the trained and the non-trained group (control group) were compared. The primary end-point was the number of adequate diagnoses of the nature of the skin lesions. The mean number of adequate diagnosis for the benign versus premalignant/malignant nature of the lesions was higher in the online training group (13.4 vs. 11.2 points; p value <0.0001). While the other knowledge scores were also significantly higher, no statistical difference was observed on the level of self-confidence between the 2 groups. In conclusion, the online formation was effective to improve the rheumatologists' ability to diagnose skin cancer

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