## 7 Discussion and Possible Extensions of this Work  
Our broad research objective is to create a computational model accurately representing medical guidelines' disagreements. Since the creation of such accurate models is beyond the current state of the art, in this article, we focused on an approximation, i.e., a model that is simple and general enough to be potentially applicable in other situations, and which was useful for the question at hand, namely, whether conceptual vocabulary determines recommendations. As mentioned earlier, this article was partly motivated by epistemology of disagreement, and more specifically medical disagreement, viewed as \"near-peer\" disagreement. Our results show that it is possible to build computational models of \"near-peer\" disagreement. Additionally, they provide support for the empirical observations of disagreement adjudication among medical experts [34; 35], where the authors observe that the differences in experts' backgrounds increase the degree of disagreement. A limitation of the article lies in testing the proposed method on a small number of case studies. In the main track, we focused on the CDC summaries of the breast cancer screening guidelines, and, in Appendix A, we discuss our experiments on the lower back pain management and hypertension guidelines. We showed that the method is robust in the case of these sample guidelines, because even with the change of metrics, the similarities remain statistically significant. However, this article only describes a few case studies, and leaves it as an open question whether it will work equally well in other cases. Thus, an obvious extension of this work would be to compare other groups of guidelines, e.g., European medical societies vs. US medical societies. We know that for years their recommendations, e.g., on managing blood cholesterol, differed. Another potential extension would be to experiment with other representations, such as more complex word and document embeddings, or with more subtle semantic representations based on entity and relationship extraction or formal models, cf. [52], and on formal modeling of contradictions, like the ones discussed in [5; 6]. This, however, would introduce several complications. Attention-based models, such as BERT, GPT or Universal Encoder [53; 54; 55], would have to be used very carefully, since they encode the order of terms in documents, and indirectly relations between words. Therefore, they would not be appropriate for the experiments described in this article. More subtle formal models, on the other hand, are very brittle, and incapable of operating on real documents, with all the complications arising from interaction between sentences and the use of discourse constructions such as lists and tables. Perhaps one solution to this problem could be to represent the full text of each guideline document as a graph, and not a bag of word embeddings. There is a vast amount of applicable empirical work on graph representations, including representations of recommendations (e.g., [56; 57]) and various types of knowledge (e.g., [58; 59]). The algorithms proposed in Section 5 would still be directly applicable, and only the distances between pairs of documents would have to be modified and computed on graph representations. These representations could vary, but in all cases we could use applicable algorithms for computing distances in graphs (e.g., [60]), similar to the word mover's distance (WMD) used in this article. In addition, by experimenting with matching corresponding subgraphs, we could develop new distance measures. Unlike our earlier work [6], in this article we have not performed any logical analysis of the guidelines. We also did not use text mining to extract relations from the content of the guidelines, and although our focus was on concepts appearing in guidelines, we did not point to specific vocabulary differences. Instead, we measured semantic differences between guidelines using the distances between their vectorial representations.