Utilization of sentiment analysis to assess and compare negative finding reporting in veterinary and human literature

Abstract

Publication bias and the decreased publication of trials with negative or non-significant results is a well-recognized problem in human and veterinary medical publications. These biases may present an incomplete picture of evidence-based clinical care and negatively impact medical practices. The purpose of this study was to utilize a novel sentiment analysis tool as a quantitative measure for assessing clinical trial reporting trends in human and veterinary medical literature. Abstracts from 177,617 clinical trials in human medical journals and 8684 in veterinary medical journals published in the PubMed database from 1995 to 2020. Abstracts were analyzed using the GAN-BioBERT sentiment classifier for both general trends and percentage of neutral/negative publications. Sentiment was defined on a − 1 (highly negative) to 1 (highly positive) scale.Human-based clinical trial publications were less likely to feature positive findings (OR 0.87, P < 0.001) and more likely to include neutral findings (OR 1.18, P < 0.001) relative to veterinary clinical trials. No difference was found in reporting of negative sentiment trials (OR 1.007, P = 0.83). In both groups, the published sentiment of clinical trials increased over time. Using sentiment analysis to evaluate large publication datasets and compare publication trends within and between groups, this study is significant in its detection of significant publication differences between human and veterinary medicine clinical trials and a continued unbalanced positive sentiment in the published literature. The implications of this unbiased reporting have important clinical and research implications that require consideration.