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Abstract The utilization of accolade awards to motivate reviewers is widespread, but their effectiveness remains uncertain. We aim to explore how receiving an accolade award affects reviewers’ subsequent number of reviews. In contrast to small-scale, unrepresentative experiments, we perform a large-scale, global, and all-disciplinary analysis based on quasi-natural experiments. By integrating the Publons, ORCID, MAG, SciSciNet, and OpenAlex databases, a large dataset that tracks the reviewers’ annual number of reviews and their bibliometric indicators is compiled, encompassing 179,794 individuals. Among them, 6605 individuals who receive the “Publons Global Peer Review Award” in 2018 are constituted as the experimental group. Those non-winners are then matched to form a control group. Using propensity score matching (PSM), 711 and 762 reviewers are matched as the experimental and control groups respectively. An analysis employing the difference-in-differences (DiD) method is conducted to examine the impact of an accolade award on reviewers’ subsequent number of reviews. It is found that, following the receipt of an accolade award, reviewers, on average, reviews about four fewer manuscripts, with the reduction exhibiting a V-shaped pattern. Additional analyses are conducted to examine how individual differences and socio-economic factors influence the awarding effects. Besides, we analyze the mechanisms underlying the awarding effect and propose strategies to motivate reviewers. In brief, the sharp decrease in marginal utility of accolades, the voluntary nature of peer review, and the unexpected properties of accolade awards are the potential mechanisms that generate the negative effect. The academic community should reassess the existing incentive strategies for reviewers.

Several vascular embolization materials are commonly used in clinical practice, however, having application defects of varying degrees, such as poor intraoperative imaging and easy recanalization of embolized blood vessels, they are challenging for application during Transcatheter arterial embolization (TAE). Thus, an intraoperative visible vascular embolization material with good embolization effect and biocompatibility can improve transcatheter arterial embolization clinical efficacy to some extent. Our study aimed to synthesize a novel vascular embolization material that can achieve complete embolization of arterial trunks and peripheral vessels, namely poly (N-isopropyl acrylamide)-co-acrylic acid nanogel (NIPAM-co-AA). Iohexol 200 mg/mL was co-assembled with 7 wt% NIPAM-co-AA nanogel to create an intelligent thermosensitive radiopaque nanogel (INCA), which achieves a good intraoperative imaging effect and is convenient for transcatheter arterial bolus injection due to its good fluidity and temperature-sensitive sol-gel phase transition. The normal rabbit kidney embolism model further confirmed that INCA could effectively use Digital subtraction angiography (DSA) to achieve intraoperative imaging, and real-time monitoring of the embolization process could avoid mis-embolization and leakage. Meanwhile, in a 42-day study, INCA demonstrated an excellent embolization effect on the right renal artery of New Zealand white rabbits, with no vascular recanalization and ischemic necrosis and calcification remaining. As a result, this radiopaque thermosensitive nanogel has the potential to be an intelligent thermosensitive medical vascular embolization material, providing dual benefits in TAE intraoperative imaging and long-term postoperative embolization while effectively addressing the shortcomings and challenges of commonly used clinical vascular embolization agents.

Predicting breakthrough papers holds great significance; however, prior studies encountered challenges in this task, indicating a need for substantial improvement. We propose that the failure to capture the dynamic structural-evolutionary features of citation networks is one of the major reasons. To overcome this limitation, this paper introduces a new method for constructing citation cascades of focus papers, allowing the creation of a time-series-like set of citation cascades. Then, through a thorough review, three types of structural indicators in these citation networks that could reflect breakthroughs are identified, including certain basic topological metrics, PageRank values, and the von Neumann graph entropy. Based on the time-series-like set of citation cascades, the dynamic trajectories of these indicators are calculated and employed as predictors. Using the Nobel Prize-winning papers as a landmark dataset, our prediction method yields approximately a 7% improvement in the ROC-AUC score compared to static-based prior methods. Additionally, our method advances in achieving earlier predictions than other previous methods. The main contribution of this paper is proposing a novel method for creating citation cascades in chronological order and confirming the significance of predicting breakthroughs from a dynamic structural perspective.

Clinical diagnosis of epilepsy significantly relies on identifying interictal epileptiform discharge (IED) in electroencephalogram (EEG). IED is generally interpreted manually, and the related process is very time-consuming. Meanwhile, the process is expert-biased, which can easily lead to missed diagnosis and misdiagnosis. In recent years, with the development of deep learning, related algorithms have been used in automatic EEG analysis, but there are still few attempts in IED detection. This study uses the currently most popular convolutional neural network (CNN) framework for EEG analysis for automatic IED detection. The research topic is transferred into a 4-labels classification problem. The algorithm is validated on the long-term EEG of 11 pediatric patients with epilepsy. The computational results confirm that the CNN-based model can obtain high classification accuracy, up to 87%. The study may provide a reference for the future application of deep learning in automatic IED detection.