What should labs inspect before using a generated review release?

Inspect a generated review release against these checks before placing it in a training or evaluation pipeline.

- Verify that referenced records exist. - Check date ordering across each history. - Inspect labels for hidden answer cues. - Separate related cases across evaluation splits. - Review coverage of uncommon valid outcomes. - Inspect grounding and disclosure risk. - Check that explanations match supporting evidence.

Relational consistency and narrative plausibility are different properties. A convincing account history can still reference a nonexistent decision, while a technically valid table can describe an implausible sequence of responsibilities. Both need attention.

Leakage can arise through generated artefacts such as outcome words embedded in filenames or closely related variants appearing in both training and evaluation. Those defects may let a model succeed without performing the intended review reasoning.

The output is generated, but generation alone does not establish anonymity or privacy compliance. Buyers should inspect how operational grounding is represented and whether any detail creates disclosure concerns. MirrorFirm could provide release documentation and inspection samples that support these checks, with unresolved issues recorded before the buyer decides how to use the material.