diff --git a/analysis/matlab/README.md b/analysis/matlab/README.md
index 8fbd77c..32af9d0 100644
--- a/analysis/matlab/README.md
+++ b/analysis/matlab/README.md
@@ -97,3 +97,13 @@ for the small, unbalanced groups in this study. What should agree is the
 condition classifies as A2-like/B2-like/ambiguous, and whether the learning
 rates differ across groups. Treat the MATLAB numbers as a sensitivity check
 on the Python's conclusions, not as a numerically-identical reproduction.
+
+## Scope vs. the Python
+
+The MATLAB port reproduces the Python's three core models (count, rate,
+learning-rate), per-animal tests, unknown-group classification, and the H2
+verdict (read off the main model's A2-vs-B2 term). The Python's two auxiliary
+sensitivity sections — `report_anchor_only` (a two-anchor-only refit) and
+`report_mixed` (a Bayesian MAP random-intercept model) — are intentionally
+**not** ported: the anchor-only refit is closely tracked by the main-model H2
+term, and MATLAB's native `fitglme` already *is* the random-intercept model.
diff --git a/analysis/matlab/tdcs_scenario_data.m b/analysis/matlab/tdcs_scenario_data.m
index 29065df..4e605ad 100644
--- a/analysis/matlab/tdcs_scenario_data.m
+++ b/analysis/matlab/tdcs_scenario_data.m
@@ -66,6 +66,8 @@ groupCell = groupCell(dayMask);
 % level). Only categories actually present after merge+window survive.
 S.group = categorical(groupCell);
 allCats = categories(S.group);
+assert(ismember(cfg.ref, allCats), 'tdcs_scenario_data:refMissing', ...
+    'Reference group "%s" is absent from scenario "%s".', cfg.ref, char(scenario));
 otherCats = allCats(~strcmp(allCats, cfg.ref));
 S.group = reordercats(S.group, [{cfg.ref}; otherCats]);