diff --git a/src/graphnet/models/standard_model.py b/src/graphnet/models/standard_model.py
index 53c32deaf..e38308ba1 100644
--- a/src/graphnet/models/standard_model.py
+++ b/src/graphnet/models/standard_model.py
@@ -413,6 +413,9 @@ def predict_as_dataframe(
             f"number of output columns ({predictions.shape[1]}) don't match."
         )
 
+        # Check if predictions are on event- or pulse-level
+        pulse_level_predictions = len(predictions) > len(dataloader.dataset)
+
         # Get additional attributes
         attributes: Dict[str, List[np.ndarray]] = OrderedDict(
             [(attr, []) for attr in additional_attributes]
@@ -426,25 +429,39 @@ def predict_as_dataframe(
                 # Check if node level predictions
                 # If true, additional attributes are repeated
                 # to make dimensions fit
-                if len(predictions) != len(dataloader.dataset):
+                if pulse_level_predictions:
                     if len(attribute) < np.sum(
                         batch.n_pulses.detach().cpu().numpy()
                     ):
                         attribute = np.repeat(
                             attribute, batch.n_pulses.detach().cpu().numpy()
                         )
-                        try:
-                            assert len(attribute) == len(batch.x)
-                        except AssertionError:
-                            self.warning_once(
-                                "Could not automatically adjust length"
-                                f"of additional attribute {attr} to match length of"
-                                f"predictions. Make sure {attr} is a graph-level or"
-                                "node-level attribute. Attribute skipped."
-                            )
-                            pass
                 attributes[attr].extend(attribute)
 
+        # Confirm that attributes match length of predictions
+        skip_attributes = []
+        for attr in attributes.keys():
+            try:
+                assert len(attributes[attr]) == len(predictions)
+            except AssertionError:
+                self.warning_once(
+                    "Could not automatically adjust length"
+                    f" of additional attribute '{attr}' to match length of"
+                    f" predictions.This error can be caused by heavy"
+                    " disagreement between number of examples in the"
+                    " dataset vs. actual events in the dataloader, e.g. "
+                    " heavy filtering of events in `collate_fn` passed to"
+                    " `dataloader`. This can also be caused by requesting"
+                    " pulse-level attributes for `Task`s that produce"
+                    " event-level predictions. Attribute skipped."
+                )
+                skip_attributes.append(attr)
+
+        # Remove bad attributes
+        for attr in skip_attributes:
+            attributes.pop(attr)
+            additional_attributes.remove(attr)
+
         data = np.concatenate(
             [predictions]
             + [