# Topic Modeling for Faster Literature Screening Using Transformer-Based Embeddings
## 2 Materials and Methods  
### Datasets  
Our analysis focused on two separate datasets that had been previously used to identify articles of interest (henceforth, \"target articles\") for two published systematic reviews in the dental field [35; 36]. The two datasets contained a list of scientific articles on peri-implantitis and bone regeneration, respectively. The authors of the systematic reviews had created these datasets through specific searches conducted across literature databases, and comprised bibliographic information about the articles, including authors, title, abstract, journal with publication date, plus keywords.  
The first dataset included 6137 articles on the treatment of peri-implantitis and was generated through a state of the art and well-detailed keyword-based search across several databases, including Medline and Embase [36]. In that systematic review, the investigators eventually identified 24 target articles that answered the following focused questions (FQ):  
FQ1: _In patients with peri-implantitis, what is the efficacy of different bone reconstructive therapies compared to access flap surgery (AFS) in terms of pocket reduction and change in bleeding and suppuration on probing (BOP and SOP), at a minimum of 12 months of follow-up?_  
FQ2: _In patients with peri-implantitis, what is the long-term (\(\geq\)12 months) performance of reconstructive therapies in terms of pocket reduction and change in BOP/SOP?_  
The second database had been used as a basis for another published systematic review by [PERSON] et al. on bone augmentation techniques [35]. The dataset used for this work comprised 5309 articles obtained through a systematic literature search to address the following FQs:  
FQ1: _In patients receiving GBR simultaneous to implant placement, what is the impact of biomaterials (membranes, grafts, bioactive factors) on the stability of peri-implant bone levels as assessed through 2D or 3D radiographs in RCTs/CCTs with \(\geq\) 12 months of follow-up?_  
FQ2: _In patients receiving GBR simultaneous to implant placement, what is the impact of biomaterials (membranes, grafts, bioactive factors) on bone defect dimension (width and/or height) changes as evaluated at re-assessment procedures performed at \(\geq\)4 months post GBR in RCTs/CCTs?_  
We manually screened these two datasets, searching for off-topic articles (henceforth labelled OffTA). We adopted a broad definition of OffTAs as those articles that did not focus on dentistry, e.g., penile prostheses or breast implants. Pre-clinical studies were not considered OffTAs unless they were investigating areas that were clearly related to fields other than dentistry. So, for instance, a report on cellular behavior in an in vitro setting would not necessarily be considered an OffTA, but a pre-clinical investigation on a fracture model in rodent would. All the papers that investigated areas of dentistry (or applicable to dentistry) were considered on-topic articles (OnTAs) for both datasets. It may be argued that when it comes to peri-implantitis, orthopedic implants may be closer to dental implants than, e.g., orthognatic surgery (which is related to dentistry), or that orthopedic research articles may be more relevant to bone regeneration of the alveolar ridges than many dental-related research areas, but as the goal of our investigation was to filter out papers from the dataset to make systematic reviews faster, we worked under the assumption that it would be safer to discard articles that focused on different clinical areas than dentistry, and that would not increase the risk of losing important pieces of evidence for both datasets.  
Upon inspection, the first dataset was composed of 3810 OnTAs (i.e., dentistry-related) and 2327 OffTAs (38%), while the second dataset appeared to include only 814 OffTAs or 15% of the total.