diff --git a/docs/source/configuration.md b/docs/source/configuration.md
index 38fcc99..ca83871 100644
--- a/docs/source/configuration.md
+++ b/docs/source/configuration.md
@@ -71,7 +71,7 @@ on its source format.
 
 1. If the source data is in [MEDS](https://github.com/Medical-Event-Data-Standard/meds) format
    (recommended), then the `code` will be checked directly against MEDS' `code` field and the `value_min`
-   and `value_max` constraints will be compared against MEDS' `numerical_value` field.
+   and `value_max` constraints will be compared against MEDS' `numeric_value` field.
 
    **Note**: This syntax does not currently support defining predicates that also rely on matching other,
    optional fields in the MEDS syntax; if this is a desired feature for you, please let us know by filing a
diff --git a/src/aces/config.py b/src/aces/config.py
index 45d76d8..b2a3183 100644
--- a/src/aces/config.py
+++ b/src/aces/config.py
@@ -45,17 +45,17 @@ def MEDS_eval_expr(self) -> pl.Expr:
         Examples:
             >>> expr = PlainPredicateConfig("BP//systolic", 120, 140, True, False).MEDS_eval_expr()
             >>> print(expr) # doctest: +NORMALIZE_WHITESPACE
-            [(col("code")) == (String(BP//systolic))].all_horizontal([[(col("numerical_value")) >=
-               (dyn int: 120)], [(col("numerical_value")) < (dyn int: 140)]])
+            [(col("code")) == (String(BP//systolic))].all_horizontal([[(col("numeric_value")) >=
+               (dyn int: 120)], [(col("numeric_value")) < (dyn int: 140)]])
             >>> cfg = PlainPredicateConfig("BP//systolic", value_min=120, value_min_inclusive=False)
             >>> expr = cfg.MEDS_eval_expr()
             >>> print(expr) # doctest: +NORMALIZE_WHITESPACE
-            [(col("code")) == (String(BP//systolic))].all_horizontal([[(col("numerical_value")) >
+            [(col("code")) == (String(BP//systolic))].all_horizontal([[(col("numeric_value")) >
                (dyn int: 120)]])
             >>> cfg = PlainPredicateConfig("BP//systolic", value_max=140, value_max_inclusive=True)
             >>> expr = cfg.MEDS_eval_expr()
             >>> print(expr) # doctest: +NORMALIZE_WHITESPACE
-            [(col("code")) == (String(BP//systolic))].all_horizontal([[(col("numerical_value")) <=
+            [(col("code")) == (String(BP//systolic))].all_horizontal([[(col("numeric_value")) <=
                (dyn int: 140)]])
             >>> cfg = PlainPredicateConfig("BP//diastolic")
             >>> expr = cfg.MEDS_eval_expr()
@@ -136,14 +136,14 @@ def MEDS_eval_expr(self) -> pl.Expr:
 
             if self.value_min is not None:
                 if self.value_min_inclusive:
-                    criteria.append(pl.col("numerical_value") >= self.value_min)
+                    criteria.append(pl.col("numeric_value") >= self.value_min)
                 else:
-                    criteria.append(pl.col("numerical_value") > self.value_min)
+                    criteria.append(pl.col("numeric_value") > self.value_min)
             if self.value_max is not None:
                 if self.value_max_inclusive:
-                    criteria.append(pl.col("numerical_value") <= self.value_max)
+                    criteria.append(pl.col("numeric_value") <= self.value_max)
                 else:
-                    criteria.append(pl.col("numerical_value") < self.value_max)
+                    criteria.append(pl.col("numeric_value") < self.value_max)
 
         if self.other_cols:
             criteria.extend([pl.col(col) == value for col, value in self.other_cols.items()])