### Brief Description of the Proposed Method  
In this article we investigate the question of whether differences in medical recommendations come from differences in specialized medical knowledge applied to specific classes of patients, and whether such differences in specialties can be modeled computationally. Figure 1: Note the contradictory recommendations in green and blue boxes. The colors in the table come from [6], but the original table comes from the CDC [3]. Only a part of the table is reproduced here. Our idea is to model \"specialized medical knowledge\", which we cannot easily observe, by the differences in vocabulary used in medical guidelines. We then show that these vocabularies, assembled in vector representations of these documents, produce the differences in recommendations. We evaluate our method using three case studies: breast cancer screening guidelines, lower back pain management guidelines and hypertension management guidelines. In the main track of this article, we use the breast cancer screening guidelines to present our approach and the evaluation, and the additional evaluations on the other two sets of guidelines are presented in the Appendix A. More specifically, we computationally compare the _full_ texts of guidelines with the their _recommendation summaries_. For breast cancer screening, the summaries come from the CDC [3]; for lower back pain management, they come from a summary article [13]; and, for hypertension management, where we lack a tabular comparison, we used the abstracts of the documents. That is, we see if the semantic similarities between the full documents follow the same pattern as semantic similarities between the summaries. Note that each computational comparison was made between two _sets_ of documents and not individual documents. This process involves several steps and is shown in Figure 2, for the breast cancer screening guidelines. Thus, the vector representations of full texts of the guidelines model the vocabularies as bags of concepts, and therefore cannot model specific recommendations: the concepts in the recommendations, such as \"mammography\" and \"recommend\", appear in _all_ full texts, but specific societies may be either for mammography or against it. The vector representations of recommendations model the differences in prescribed procedures, but not the vocabularies (see Tables 1 and 2 below). \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Guideline** & **40–49** & **50–74** & **75+** & **Dense Breast** & **Higher than Average Risk** \\ \hline
**AAFP** & b & r & b & b & N \\ \hline
**ACOG** & r & r & b & b & r \\ \hline
**ACP** & b & r & r & N & N \\ \hline
**ACR** & r & r & r & b & r \\ \hline
**ACS** & b & r & r & b & b \\ \hline
**IARC** & b & r & N & b & r \\ \hline
**USPSTF** & b & r & b & b & r \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The table shows recommendations as follows: N—no recommendation; b—both patient and doctor, shared decision; r—recommending mammography. Figure 2: The method of comparing concepts in full documents and recommendations contained in summaries. Note the difference in representations: the document are represented by a large number of high-dimensional (200) vectors with real valued features, whereas the disagreement representations can be low-dimensional vectors with discrete features (e.g., five-dimensional for the breast cancer screening guidelines). Our exposition will roughly follow the left-to-right order of this figure, using the breast cancer screening guidelines as the motivating example. How do we know if vocabularies determine recommendations? We compute pairwise distances (cosine or word mover's distance) between the full text vectors. In parallel, we compute pairwise distances between the recommendation vectors. We thus get two graphs, and their shapes can be compared. We show that the resulting geometries are very similar and could not have been produced by chance.