What activation patching localizes can depend on where the patch is applied. Reproducing Ahsan et al. (2025) and extending it from four to ten models, we find the behavioral results largely reproduce and that medical adaptation improves calibration consistently only for gender with BioMistral, for MedGemma not at all. Two mechanistic findings are artifacts of the patch site. In Gemma-2 and OLMo-2 the patched MLP down-projection passes through a scale-invariant RMSNorm before the residual stream, so activation scaling is ineffective there. Patching after normalization restores it, raising the Gemma-2 flip rate on multiple sclerosis from 0.83, flat under scaling, to 1.00. It also lets a single layer recover the target race nearly as well as the reported sliding window (0.83 vs. 0.84, OLMo-2), so the window compensated for the site, not a distributed representation.