diff --git a/evaluation_time_dependent.py b/evaluation_time_dependent.py
index 9e6f2f2..955b02b 100644
--- a/evaluation_time_dependent.py
+++ b/evaluation_time_dependent.py
@@ -188,8 +188,9 @@ def evaluate_time_dependent(
 
         h = model(event_seq, time_seq, sexes, cont_feats, cate_feats)  # (B,L,D)
 
-        # Context index selection (independent of horizon); keep mask is refined per horizon.
-        keep0, t_ctx, _ = select_context_indices(
+        # Select a single fixed context per sample for this batch.
+        # Horizon-specific eligibility is derived from this context (do not re-select per horizon).
+        keep0, t_ctx, t_ctx_time = select_context_indices(
             event_seq=event_seq,
             time_seq=time_seq,
             offset_years=float(cfg.offset_years),
@@ -211,15 +212,20 @@ def evaluate_time_dependent(
                 f"criterion.calculate_cifs must return (B,K,T) when taus is (T,), got shape={tuple(cifs_all.shape)}"
             )
 
+        # Follow-up end time per sample = time at last valid token.
+        valid = event_seq != 0
+        lengths = valid.sum(dim=1)
+        last_idx = torch.clamp(lengths - 1, min=0)
+        followup_end_time = time_seq[b, last_idx]
+
         for h_idx, tau_y in enumerate(horizons_years):
-            keep, _, _ = select_context_indices(
-                event_seq=event_seq,
-                time_seq=time_seq,
-                offset_years=float(cfg.offset_years),
-                tau_years=float(tau_y),
+            # Horizon-specific eligibility without reselecting context:
+            # keep_tau = keep0 & (followup_end_time >= t_ctx_time + tau)
+            keep_tau = keep0 & (
+                followup_end_time >= (
+                    t_ctx_time + (float(tau_y) * DAYS_PER_YEAR))
             )
-            keep = keep & keep0
-            if not keep.any():
+            if not keep_tau.any():
                 continue
 
             if cause_ids is None:
@@ -230,8 +236,8 @@ def evaluate_time_dependent(
                     tau_years=float(tau_y),
                     n_disease=n_disease,
                 )
-                y = y[keep]
-                preds = cifs_all[keep, :, h_idx]
+                y = y[keep_tau]
+                preds = cifs_all[keep_tau, :, h_idx]
             else:
                 y = multi_hot_selected_causes_within_horizon(
                     event_seq=event_seq,
@@ -241,8 +247,8 @@ def evaluate_time_dependent(
                     cause_ids=cause_ids,
                     n_disease=n_disease,
                 )
-                y = y[keep]
-                preds = cifs_all[keep, :, h_idx].index_select(
+                y = y[keep_tau]
+                preds = cifs_all[keep_tau, :, h_idx].index_select(
                     dim=1, index=cause_ids)
 
             y_true_by_h[h_idx].append(y.detach().to(torch.bool).cpu().numpy())