To further assess the use of test-time augmentation, we analysed the in-between model and in-between method distances (Figure ). Figure indicates that both the in-between model distance and the in-between method distances are reduced when values are averaged using test-time augmentation. This means that values become more consistent when using the average over test-time augmentation between both methods and models. We further investigated whether this effect was because of consistency or overall reduction in information by investigating the entropy of averaged values and canonical values (Figure ). There was no difference found between averaged values or canonical values, indicating only consistency between methods change, not specific attributions. Finally, we analyse the robustness of model explanation by examining the overall attribution of all tokens as opposed to tokens corresponding to expert-derived structural alerts (Figure ). Values of IG, SHAP and AttGrads all consistently had the highest values with mean values around 0.2, whereas a Attention Maps, Rollout and CAT gave consistently similar or slightly lower relative importance. Relative importance of Grads and AttCAT consistently gave the lowest relative attribution to the structural alerts. Relative importance is generally consistent between randomized models, in-domain samples, training variations and test-time augmentation averaging. Similar consistent results were observed in model variations (Figure ).