diff --git a/compute_burden_index_landmarks.py b/compute_burden_index_landmarks.py
index 3b33f98..d26dd86 100644
--- a/compute_burden_index_landmarks.py
+++ b/compute_burden_index_landmarks.py
@@ -235,7 +235,6 @@ def _row_to_worker_spec(row: dict[str, Any]) -> dict[str, Any]:
         "patient_id": int(row["patient_id"]),
         "dataset_index": int(row["dataset_index"]),
         "landmark_age": float(row["landmark_age"]),
-        "t_query": float(row["t_query"]),
         "followup_end_time": float(row["followup_end_time"]),
     }
 
@@ -253,14 +252,19 @@ def _materialize_worker_rows(
         tgt_time = np.asarray(sample["target_time_seq"], dtype=np.float32)
         full_event = np.concatenate([seq_event, tgt_event[-1:]])
         full_time = np.concatenate([seq_time, tgt_time[-1:]])
-        t_query = np.float32(float(spec["t_query"]))
+        t_query = np.float32(float(spec["landmark_age"]))
         prefix_mask = full_time <= t_query
+        landmark_age = np.float32(float(spec["landmark_age"]))
+        if landmark_age != t_query:
+            raise RuntimeError(
+                f"landmark_age and t_query diverged for dataset_index={spec['dataset_index']}"
+            )
         rows.append(
             {
                 "patient_id": int(spec["patient_id"]),
                 "dataset_index": int(spec["dataset_index"]),
                 "sex": int(sample["sex"]),
-                "landmark_age": np.float32(float(spec["landmark_age"])),
+                "landmark_age": landmark_age,
                 "t_query": t_query,
                 "followup_end_time": np.float32(float(spec["followup_end_time"])),
                 "event_seq": full_event[prefix_mask].astype(np.int64, copy=False),
@@ -558,6 +562,7 @@ def _project_bi_rows(
 
     out: list[dict[str, Any]] = []
     for row_idx, row in enumerate(rows):
+        query_time = float(row["t_query"])
         for item in projected:
             matrix = item["matrix"]
             historical = item["historical"][row_idx]
@@ -569,8 +574,8 @@ def _project_bi_rows(
                         "patient_id": row["patient_id"],
                         "dataset_index": row["dataset_index"],
                         "sex": row["sex"],
-                        "landmark_age": float(row["landmark_age"]),
-                        "t_query": float(row["t_query"]),
+                        "landmark_age": query_time,
+                        "t_query": query_time,
                         "followup_end_time": float(row["followup_end_time"]),
                         "horizon": float(horizon),
                         "formed_mode": formed_mode,