Endoscopic ultrasound (EUS) is widely used as a cost-effective method for detecting pancreatic neuroendocrine tumors (PNTs), but its diagnostic value is variable among published studies. This meta-analysis aimed to determine the diagnostic value of EUS for PNTs.
Three electronic databases, including PubMed, Embase, and the Cochrane Library, were searched for studies published up to July 2018. The summary sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and receiver operating characteristic (ROC) curve were calculated to evaluate the diagnostic value of EUS using the random-effects model.
Thirteen studies involving 609 patients were included in this meta-analysis. The summary sensitivity and specificity of EUS for detecting PNTs were 0.86 and 0.89, respectively. The PLR and NLR of EUS were 7.81 and 0.15, respectively. The DOR of EUS for diagnosing PNTs was 24.20. The area under the ROC was 0.90. Finally, the subgroup analyses indicated that publication year and percentage of males could introduce potential biases for the DOR of EUS.
This meta-analysis suggests that EUS has a relatively high diagnostic value for diagnosing PNTs.

Social media has become a new source for obtaining real-world data on adverse drug reactions. Many studies have investigated the use of social media to detect early signals of adverse drug reactions. However, the trustworthiness of signals derived from social media is questionable. To confirm this, a confirmatory study with a positive control (eg, new black box warnings, labeling changes, or withdrawals) is required.
This study aimed to evaluate the use of social media in detecting new black box warnings, labeling changes, or withdrawals in advance.
This scoping review adhered to the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews checklist. A researcher searched PubMed and EMBASE in January 2021. Original studies analyzing black box warnings, labeling changes, or withdrawals from social media were selected, and the results of the studies were summarized.
A total of 14 studies were included in this scoping review. Most studies (8/14, 57.1%%) collected data from a single source, and 10 (71.4%) used specialized health care social networks and forums. The analytical methods used in these studies varied considerably. Three studies (21.4%) manually annotated posts, while 5 (35.7%) adopted machine learning algorithms. Nine studies (64.2%) concluded that social media could detect signals 3 months to 9 years before action from regulatory authorities. Most of these studies (8/9, 88.9%) were conducted on specialized health care social networks and forums. On the contrary, 5 (35.7%) studies yielded modest or negative results. Of these, 2 (40%) used generic social networking sites, 2 (40%) used specialized health care networks and forums, and 1 (20%) used both generic social networking sites and specialized health care social networks and forums. The most recently published study recommends not using social media for pharmacovigilance. Several challenges remain in using social media for pharmacovigilance regarding coverage, data quality, and analytic processing.
Social media, along with conventional pharmacovigilance measures, can be used to detect signals associated with new black box warnings, labeling changes, or withdrawals. Several challenges remain; however, social media will be useful for signal detection of frequently mentioned drugs in specialized health care social networks and forums. Further studies are required to advance natural language processing and mine real-world data on social media.