# Topic Modeling for Faster Literature Screening Using Transformer-Based Embeddings
## 3 Results and Discussion
### Peri-Implantitis Dataset Analysis
However, this dataset also included at least 8 completely unrelated OffTGs, some of them quite conspicuous in size, such as the following:  
#1 Valves and Stents in Coronary Arteries (n = 587), e.g., \"Carotid-subclavian bypass grafting with polytetrafluoroethylene grafts for symptomatic subclavian artery stenosis or occlusion: a 20-year experience\" [61];  
#2 Intraocular Lens Inflammation (n = 232), e.g., \"Double-masked, placebo-controlled evaluation of loteprednol etabonate 0.5 for postoperative inflammation\" [62];  
#5 Parkinson's Disease and Deep Brain Stimulation (n = 174), e.g., \"Three-dimensional space fluid-attenuated inversion recovery at t to improve subthalamic nucleus lead placement for deep brain stimulation in Parkinson's disease: from preclinical to clinical studies\" [63];  
#16 Cochlear Implantation (n = 96), e.g., \"Online support group users' perceptions and experiences of bone-anchored hearing aids (bahas): a qualitative study\" [64]. A closer examination of the topic list suggests that more topics could be considered unrelated to the query, albeit dental-related. Some of them are small niche topics, such as #12 Zirconia Implants and Abutments (n = 61), some are larger, such as topic #5 Sinus Floor Elevation (n = 144); although it is thematically related to implants, nothing in its keyword descriptors mentions peri-implant disease:  
['sinus floor','sinus elevation', 'osteotome sinus','sinus augmentation','sinus surgery','maxillary sinus','sinus implants','sinus lift', 'transcrestal sinus', 'eluting sinus']  
Similarly to identifying OffTAs, the decision to discard OffTGs based on their descriptors is subjective, and there is a degree of risk in discarding dentistry- or implant-related topics. The domain knowledge of the investigator is likely the main factor affecting where to place the threshold, i.e., to decide which topics to retain and which ones to discard. The algorithm we employed makes no assumption on the relevance of the identified topics but merely segments the dataset into groups and labels them. It is up to the investigators to decide whether a topic is relevant to the theme of the query. We adopted a cautious approach by retaining all dental-related topics, and we thus decided to discard only those topics that were unrelated to dentistry as a whole to avoid risking losing relevant articles. To better understand how the titles of the dataset were semantically distributed, we reduced the embedding dimensionality that we used for topic modeling from 5 down to 2 dimensions, so that each title could be represented as a data point in a scatter plot (Figure 3). Closer points correspond to articles whose titles have closer meaning and, therefore. topic, while farther points represent articles that belong to less closely related topics, including frank OffTAs. As can be easily noticed, the conceptual space for this dataset of scientific publications is not homogeneous, but there are several areas of higher density, which constitute the individual topics, and some topics appear isolated. Unsurprisingly, OffTAs tend to be distributed peripherally, as a satellite constellation of articles with looser association to the core of the dataset, which appears in the middle of the plot and includes most of the dental-related articles (Figure 3).