"a hybrid methodology has emerged [18]. It cleverly marries the rapid categorization capabilities of zero-shot classification with the precision of traditional machine learning models such as SVM and RF. Here, zero-shot classification provides an initial curation of all abstracts in the training set. Then, based on zero-shot classification’s curation, the abstracts with classification score higher than a pre-specified threshold are selected to undergo manual review to rectify zero-shot classification. The rectified classification then serves as a foundation for training traditional models, which are then employed for screening the broader dataset (e.g., in the testing set). The hybrid approach balances the speed of zero-shot and the precision of traditional ML, and potentially offers enhanced accuracy at reduced human efforts. However, this approach involves the use of multiple methodologies and still relies on well-curated, labeled training data...."

Table 2 Results on the Bannach-Brown 2016 database

Table 3 Results on the Meijboom 2021 database

Table 4 Results on the Menon 2022 database