## 8 Conclusions  
This article investigates the question whether the disagreements in medical recommendations, for example in breast cancer screening or back pain management guidelines, can be attributed to the differences in concepts brought to the problem by specific medical societies (and not, e.g., the style or formalization of recommendations). Our experiments answered this question in the affirmative, and showed that a simple model using word embeddings to represent concepts can account for about 70% to 85% of disagreements in the recommendations. Another contribution is the articulation of a very natural graph clique-based algorithm/method for comparing the similarity of two collections of documents. Given two sets of documents, each of the same cardinality, and a mapping between nodes, we computed the percent of distortion between the shapes of the two cliques, and the chances that the mapping arose from a random process. We also documented all of the steps of the process and provided the data and the code ([[https://github.com/hematialam/Conceptual_Distances_Medical_Recommendations](https://github.com/hematialam/Conceptual_Distances_Medical_Recommendations)]([https://github.com/hematialam/Conceptual_Distances_Medical_Recommendations](https://github.com/hematialam/Conceptual_Distances_Medical_Recommendations)) (accessed on 24 February 2021)) to facilitate both extensions of this work and its replication. Our work extends the state-of-the-art computational analysis of medical guidelines. Namely, instead of semi-automated conceptual analysis, we demonstrated the feasibility of automated conceptual analysis. That is, in our study, we used a representation derived from a (relatively shallow) neural network (BioASQ embeddings [51]), and knowledge-based annotations derived from MetaMap ([[https://metamap.nlm.nih.gov/](https://metamap.nlm.nih.gov/)]([https://metamap.nlm.nih.gov/](https://metamap.nlm.nih.gov/)) (accessed on 24 February 2021)). Our results, detailed in Section 6 and in Appendix A, show that both can be useful as representations of our set of guidelines. Overall, they show similar performance in modeling conceptual similarities. However, the BioAsq_WMD model, using the BioASQ embeddings and the Word Mover's Distance, seems to be most stable, as it performed very well in all our experiments. Although this article is a collection of three case studies, bound by a common method, it could be a good starting point for an analysis of other medical guidelines and perhaps other areas of expert disagreement. The methods described in this article are easy to use and rely on well-known tools such as word embeddings and MetaMap. They can also be extended and improved to produce more accurate and deeper analyses, due to the fast progress in text mining and deep learning techniques. From the point of view of methodology of analyzing medical guidelines, this article contains the first computational implementation of the \"near-peer\" model mentioned earlier. To our knowledge, ours is the first proposal to use automated methods of text analysis to investigate differences in recommendations. **Author Contributions:** [PERSON] performed the majority of experiments discussed in this article, and substantially contributed to writing. The idea to computationally investigate the conceptual difference between the guidelines comes from [PERSON]; she also contributed with graphing and overall analysis and writing. [PERSON] performed several experiments allowing us to progress with the evaluation. [PERSON] provided the project supervision, suggested the clique-based method for computing distortions, and did the majority of the writing. The overall progress was achieved during multiple team discussions, with equal contributions. This research has not been supported by a grant. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding.