diff --git a/mcbackend/__init__.py b/mcbackend/__init__.py
index 5b9800b..7ffdf23 100644
--- a/mcbackend/__init__.py
+++ b/mcbackend/__init__.py
@@ -12,7 +12,7 @@
 except ModuleNotFoundError:
     pass
 
-__version__ = "0.5.1"
+__version__ = "0.5.2"
 __all__ = [
     "NumPyBackend",
     "Backend",
diff --git a/mcbackend/core.py b/mcbackend/core.py
index 30593fa..85970b8 100644
--- a/mcbackend/core.py
+++ b/mcbackend/core.py
@@ -23,6 +23,9 @@
 _log = logging.getLogger(__file__)
 
 
+__all__ = ("is_rigid", "chain_id", "Chain", "Run", "Backend")
+
+
 def is_rigid(nshape: Optional[Shape]):
     """Determines wheather the shape is constant.
 
@@ -133,6 +136,20 @@ def sample_stats(self) -> Dict[str, Variable]:
         return {var.name: var for var in self.rmeta.sample_stats}
 
 
+def get_tune_mask(chain: Chain, slc: slice = slice(None)) -> numpy.ndarray:
+    """Load the tuning mask from either a ``"tune"``, or a ``"*__tune"`` stat.
+
+    Raises
+    ------
+    KeyError
+        When no matching stat is found.
+    """
+    for sname in chain.sample_stats:
+        if sname.endswith("__tune") or sname == "tune":
+            return chain.get_stats(sname, slc).astype(bool)
+    raise KeyError("No tune stat found.")
+
+
 class Run:
     """A handle on one MCMC run."""
 
@@ -231,14 +248,15 @@ def to_inferencedata(self, *, equalize_chain_lengths: bool = True, **kwargs) ->
                 slc = slice(0, min_clen)
 
             # Obtain a mask by which draws can be split into warmup/posterior
-            if "tune" in chain.sample_stats:
-                tune = chain.get_stats("tune", slc).astype(bool)
-            else:
+            try:
+                # Use the same slice to avoid shape issues in case the chain is still active
+                tune = get_tune_mask(chain, slc)
+            except KeyError:
                 if c == 0:
                     _log.warning(
                         "No 'tune' stat found. Assuming all iterations are posterior draws."
                     )
-                tune = numpy.full((chain_lengths[chain.cid],), False)
+                tune = numpy.full((slc.stop,), False)
 
             # Split all variables draws into warmup/posterior
             for var in variables: