diff --git a/hlink/linking/model_exploration/link_step_train_test_models.py b/hlink/linking/model_exploration/link_step_train_test_models.py
index 78f7f21..b3859f6 100644
--- a/hlink/linking/model_exploration/link_step_train_test_models.py
+++ b/hlink/linking/model_exploration/link_step_train_test_models.py
@@ -141,14 +141,18 @@ def make_threshold_matrix(self) -> list[list[float]]:
 # Both training and test results can be captured in this type
 @dataclass(kw_only=True)
 class ThresholdTestResult:
+    model_id: str
+    alpha_threshold: float
+    threshold_ratio: float
+    true_pos: int
+    true_neg: int
+    false_pos: int
+    false_neg: int
     precision: float
     recall: float
     mcc: float
     f_measure: float
     pr_auc: float
-    model_id: str
-    alpha_threshold: float
-    threshold_ratio: float
 
 
 class LinkStepTrainTestModels(LinkStep):
@@ -654,25 +658,29 @@ def _capture_prediction_results(
         predictions.createOrReplaceTempView(f"{table_prefix}predictions")
 
         (
-            tp_count,
-            fp_count,
-            fn_count,
-            tn_count,
+            true_pos,
+            false_pos,
+            false_neg,
+            true_neg,
         ) = _get_confusion_matrix(predictions, dep_var)
-        precision = metrics_core.precision(tp_count, fp_count)
-        recall = metrics_core.recall(tp_count, fn_count)
-        mcc = metrics_core.mcc(tp_count, tn_count, fp_count, fn_count)
-        f_measure = metrics_core.f_measure(tp_count, fp_count, fn_count)
+        precision = metrics_core.precision(true_pos, false_pos)
+        recall = metrics_core.recall(true_pos, false_neg)
+        mcc = metrics_core.mcc(true_pos, true_neg, false_pos, false_neg)
+        f_measure = metrics_core.f_measure(true_pos, false_pos, false_neg)
 
         result = ThresholdTestResult(
+            model_id=model,
+            alpha_threshold=alpha_threshold,
+            threshold_ratio=threshold_ratio,
+            true_pos=true_pos,
+            true_neg=true_neg,
+            false_pos=false_pos,
+            false_neg=false_neg,
             precision=precision,
             recall=recall,
             mcc=mcc,
             f_measure=f_measure,
             pr_auc=pr_auc,
-            model_id=model,
-            alpha_threshold=alpha_threshold,
-            threshold_ratio=threshold_ratio,
         )
 
         return result
@@ -746,24 +754,23 @@ def _get_confusion_matrix(
     confusion matrix is the count of true positives, false positives, false
     negatives, and true negatives for the predictions.
 
-    Return a tuple (true_positives, false_positives, false_negatives,
-    true_negatives).
+    Return a tuple (true_pos, false_pos, false_neg, true_neg).
     """
     prediction_col = col("prediction")
     label_col = col(dep_var)
 
     confusion_matrix = predictions.select(
-        count_if((label_col == 1) & (prediction_col == 1)).alias("true_positives"),
-        count_if((label_col == 0) & (prediction_col == 1)).alias("false_positives"),
-        count_if((label_col == 1) & (prediction_col == 0)).alias("false_negatives"),
-        count_if((label_col == 0) & (prediction_col == 0)).alias("true_negatives"),
+        count_if((label_col == 1) & (prediction_col == 1)).alias("true_pos"),
+        count_if((label_col == 0) & (prediction_col == 1)).alias("false_pos"),
+        count_if((label_col == 1) & (prediction_col == 0)).alias("false_neg"),
+        count_if((label_col == 0) & (prediction_col == 0)).alias("true_neg"),
     )
     [confusion_row] = confusion_matrix.collect()
     return (
-        confusion_row.true_positives,
-        confusion_row.false_positives,
-        confusion_row.false_negatives,
-        confusion_row.true_negatives,
+        confusion_row.true_pos,
+        confusion_row.false_pos,
+        confusion_row.false_neg,
+        confusion_row.true_neg,
     )