# Topic Modeling for Faster Literature Screening Using Transformer-Based Embeddings
## 3 Results and Discussion
### Bone Augmentation Dataset Analysis  
We again applied BERTopic on the titles of the articles of the second dataset and tuned the clustering parameters as previously described to obtain an approachable number of topics. As shown in Figure 5, in general, decreasing the minimum size of the acceptable clusters in the HDBSCAN algorithm again increased the number of topics identified by BERTopic. Figure 4: Barchart representing the allocation of the target articles in the peri-implantitis dataset by BERTopic. At the same time, adjusting the UMAP parameters allows BERTopic to better recognize local features in the distribution of the data points. We maintained the same parameters as for the first dataset, which yielded 22 topics. Table 3 lists the topics identified in this dataset. The topics were visually inspected, and based on their LLM description and their keyword descriptors, we assessed that most topics were, as expected, related to bone  
Figure 5: Lineplot showing the relation between the minimum cluster size setting for HDBSCAN and the number of topics identified by BERTopic in the bone regeneration dataset, based on the number of neighbors setting in the UMAP dimension reduction algorithm. Red line: n_neighbors = 10; Blue line: n_neighbors = 15; Orange line: n_neighbors = 50; Green line: n_neighbors = 100. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Topic** & **Count** & **LLM** \\ \hline \(-\)1 & 1515 & Dental Implant Studies \\ 0 & 727 & Dental Implants in Edmunds Patients \\ 1 & 692 & Sinus Floor Elevation \\ 2 & **296** & **Hip Arthoplasty** \\ 3 & 265 & Alveolar Ridge Augmentation Techniques \\ 4 & 225 & Titanium Implants Surface Acid-Etching Osese \\ 5 & 167 & Ridge Preservation Bone Allograft \\ 6 & 147 & Bone Carting for Dental Implants \\ 7 & 146 & Soft Tissue Augmentation for Dental Implants \\ 8 & 140 & Peri-Implantitis Treatment \\ 9 & 138 & Implants in Fresh Extraction Sockets \\ **10** & **117** & **Venticular Assist Devices** \\ 11 & 102 & Platelet-Rich Fibrin Effects on Dental Implants \\ **12** & **93** & **Cervical and lumbar Fusion Studies** \\ 13 & 89 & Bone Regeneration Recombinant Human BMP \\ 14 & 81 & Hydroxyapatite-coated Dental Implant Studies \\ 15 & 73 & Periodontal defect treatment \\ 16 & 71 & Collagen Membranes for Guided Bone Regeneration \\ 17 & 64 & Implant- retained mandibular Overdentures \\ 18 & 57 & Antibiotic Prophylaxis for Dental Implants \\ **19** & **54** & **Bone Anchored Hearing Implant** \\ **20** & **50** & **Orbital and Retinal Implants** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The list of topics identified by BERTopic, in order of size, for the bone augmentation dataset. Non-dental topics are highlighted in bold. The full data are found in Supplementary Table S2. regeneration (e.g., topic #3 Alveolar Ridge Augmentation Techniques), ridge preservation (e.g., topic #5 Ridge Preservation Bone Allograft), or otherwise implant-related (e.g., topic #9 Implants in Fresh Extraction Sockets). However, a careful inspection of the dataset also revealed five OffTGs that appeared completely unrelated to the dental field and that altogether totaled 610 OffTAs (Table 3, bold). These topics included the following:  
#2 Hip Arthroplasty (n = 296), e.g., \"Timing of tibial tubercle osteotomy in two-stage revision of infected total knee arthroplasty does not affect union and reinfection rate.