# Topic Modeling for Faster Literature Screening Using Transformer-Based Embeddings
## 3 Results and Discussion
### Bone Augmentation Dataset Analysis
conducted, relaxing the need for more stringent queries and allowing for broader, more heterogeneous datasets that can be filtered using semantic-based algorithms before manual screening. This stands in contrast to the previous approaches to literature searches, which have mostly relied on complex search strategies to minimize the retrieval of heterogeneous data [134; 135; 136]. We used the all-mpnet-base-v2 model for our work, which is a general model trained on a very wide corpus of texts. BERTopic, however, is independent of the embedding model, and as new models become available, these can be used to improve the effectiveness of the approach. Specific models, trained on corpora targeting specific science areas, could also be used, if the investigators deem it necessary, to better capture the possible meaning nuances of certain science niches. One could argue that abstracts could provide more information on topic and context that just titles, and topic modeling should therefore be rather conducted on abstracts or a title + abstract combination. Of course, abstracts arguably provide more details on the content of a manuscript and work as well as or even better than titles to capture the theme of a report. Our choice of using titles for the topic-modeling analysis was rather based on the performance of the proposed algorithm. Abstracts are considerably longer than titles, and processing them takes much longer than analyzing titles, even if using hardware acceleration. One of the purposes of our work was to prove the feasibility of semantic title screening as an acceptable compromise to filter out off-topic articles prior to screening. Our approach, which relies on titles alone, could be easily scaled up to much bigger datasets, paving the way to changes in the paradigms of how literature searches are managed as a whole. In fact, our brief analysis also suggests that some topics might even be positively selected to conduct a more restricted and focused screening, with some caveats. In our situation, it was easy to retrospectively identify the topics where the target articles were contained but implementing that with a new dataset would not be as straightforward, because many topics would be about closely related areas, and excluding them would be risky. As an example of this, considering a hypothetical adoption of BERTopic in the pre-screening phase of the bone regeneration dataset for a systematic review, topic #16 Collagen Membranes for Guided Bone Regeneration could have been an easy pick for further assessment, but at this stage, there would have been no rationale to safely exclude, e.g., the quite large (n = 692) #1 Sinus Floor Elevation group by just looking at its LLM descriptor or its keywords. One final aspect to consider is the knowledge and language barrier that this procedure still poses to many life science investigators. At the present moment, applying this and similar algorithms using command line interfaces still requires a degree of coding literacy that may discourage many investigators in biology and medicine, although unjustifiably. We encourage investigators to become familiar with coding interfaces such as Google Colab or Jupyter notebooks, as they often provide an affordable alternative to proprietary software and offer a quick way to deploy, customize, and maximize the potential of new algorithms. Given the rapid advancements in machine learning and artificial intelligence, relying solely on point-and-click user interfaces may be a risky choice. Investing time in learning basic Python syntax may be a more sustainable approach to keep up with the current technological revolution. To assist those interested in trying out the algorithm used in this study, we have made a simplified version of code available as a ipynb notebook in the Supplementary Materials.