diff --git a/summit/strategies/base.py b/summit/strategies/base.py
index 1b7860aa..b17e9f55 100644
--- a/summit/strategies/base.py
+++ b/summit/strategies/base.py
@@ -104,35 +104,12 @@ def transform_inputs_outputs(self, ds: DataSet, **kwargs):
             ):
                 # Add descriptors to the dataset
                 var_descriptor_names = variable.ds.data_columns
-                if all(
-                    np.isin(var_descriptor_names, new_ds.columns.levels[0].to_list())
-                ):
-                    # Make the descriptors columns a metadata column
-                    column_list_1 = new_ds.columns.levels[0].to_list()
-                    ix = [
-                        column_list_1.index(d_name) for d_name in var_descriptor_names
-                    ]
-                    column_codes_2 = list(new_ds.columns.codes[1])
-                    ix_code = [
-                        np.where(new_ds.columns.codes[0] == tmp_ix)[0][0]
-                        for tmp_ix in ix
-                    ]
-                    for ixc in ix_code:
-                        column_codes_2[ixc] = 0
-                    new_ds.columns = new_ds.columns.set_codes(column_codes_2, level=1)
-                else:
-                    indices = new_ds[variable.name].values
-                    descriptors = variable.ds.loc[indices]
-                    descriptors.index = new_ds.index
-                    new_ds = new_ds.join(descriptors, how="inner")
-
-                # Make the original descriptors column a metadata column
-                column_list_1 = new_ds.columns.levels[0].to_list()
-                ix = column_list_1.index(variable.name)
-                column_codes_2 = list(new_ds.columns.codes[1])
-                ix_code = np.where(new_ds.columns.codes[0] == ix)[0][0]
-                column_codes_2[ix_code] = 1
-                new_ds.columns =  new_ds.columns.set_codes(column_codes_2, level=1)
+                new_ds = new_ds.merge(variable.ds, left_on=variable.name, right_index=True, how="left")
+
+                # Make the original categorical column a metadata column
+                original_categorical = new_ds[variable.name].copy()
+                new_ds = new_ds.drop((variable.name, "DATA"), axis=1)
+                new_ds[variable.name, "METADATA"] = original_categorical
 
                 # Normalize descriptors between 0 and 1
                 if min_max_scale_inputs: