diff --git a/src/predval/evaluate.py b/src/predval/evaluate.py
index 2268207..09b0af6 100644
--- a/src/predval/evaluate.py
+++ b/src/predval/evaluate.py
@@ -95,9 +95,14 @@ def coverage_delta(metrics: pd.DataFrame) -> pd.DataFrame:
     # As-published only: coverage restriction is a property of rung0, and mixing recalibrated
     # rungs into the pivot would compare corrections rather than the effect of the common subset.
     primary = metrics[(metrics["ci_method"] != "naive_row_bootstrap") & (metrics["rung"] == RUNG0)]
-    wide = primary.pivot_table(
-        index=keys, columns="subset", values="value", aggfunc="first", dropna=False
-    ).reset_index()
+    # groupby, not pivot_table: pivot_table(dropna=False) takes the cartesian product of the index
+    # levels, inventing (overall, perturbation=dialect, ...) rows that duplicate each model_id.
+    wide = (
+        primary.groupby([*keys, "subset"], dropna=False, observed=True)["value"]
+        .first()
+        .unstack("subset")
+        .reset_index()
+    )
     if "full" not in wide or "common" not in wide:
         return pd.DataFrame(columns=[*keys, "cov_delta"])
     wide["cov_delta"] = wide["full"] - wide["common"]
diff --git a/tests/test_evaluate.py b/tests/test_evaluate.py
index 1a82155..c89b769 100644
--- a/tests/test_evaluate.py
+++ b/tests/test_evaluate.py
@@ -242,6 +242,22 @@ def test_coverage_delta_is_zero_when_coverage_is_identical(result) -> None:
     assert np.allclose(finite, 0.0), "full == common here, so every delta must be zero"
 
 
+def test_coverage_delta_has_one_overall_row_per_model_with_ragged_coverage(tmp_path: Path) -> None:
+    """Subgroups plus unequal coverage must not multiply the overall rows (report.py indexes them).
+
+    pivot_table(dropna=False) crossed the index levels, inventing overall rows carrying subgroup
+    levels; _primary_rows then got a Series back from .get() and crashed rendering the report.
+    """
+    cohort, preds = build(tmp_path, drop_for_weak=40)
+    delta = coverage_delta(evaluate(cohort, preds).metrics)
+    overall = delta[(delta["stratum_kind"] == "overall") & (delta["metric"] == "auroc")]
+    assert sorted(overall["model_id"]) == sorted(MODELS)
+    assert overall["subgroup_name"].isna().all()
+    by_model = overall.set_index("model_id")["cov_delta"]
+    assert by_model["weak"] == 0.0, "weak's own rows define the common subset"
+    assert np.isfinite(by_model["good"]) and by_model["good"] != 0.0, "good loses rows to common"
+
+
 # -------------------------------------------------------------------------------- the manifest