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We have shown that transitions between items within a sequence are very small in real-world data because of gaps due to omitted entries. However, using synthetic data, we showed that there are significant differences between transitions between two items that are adjacent within a sequence and those between a pair of items occurring farther apart in time. We present these results in detail later in the article.

Our third and final aim was to investigate whether the sequence detection algorithm is capable of picking up sequences across vignettes of different complexity. In the clinical setting, sequences are rarely the only sequence of interest. We therefore performed two analyses in which we simulated different numbers of “dense high-density plaque” vignettes followed by “active-state” sequence-like vignettes. Our primary hypothesis was that the algorithm would still be able to pick up sequences despite the presence of high-density plaques. We estimated that the success rate of the algorithm would be at least 75% in these cases. We also performed a secondary exploratory analysis of whether the algorithm was capable of distinguishing between the two states, taking into account the limited reliability of the individual items: the assumption was that sequences should contain more reliable items than sparse vignettes. Finally, since BMSs tend to deplete more strongly with time, we tested whether the algorithm is able to detect faster-resorbing sequences. All analyses are presented in detail in the online-only Data Supplement.

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