A methodological framework for real-world performance studies of clinical variant classification platforms at early organizational stages.

A methodological framework for real-world performance studies of clinical variant classification platforms at early organizational stages is proposed, involving 97 cases across three classifier versions.

• Why it matters: Current methods for evaluating variant classification platforms are limited at early stages, often relying on poorly defended single-source comparisons or requiring complex regulatory infrastructure, which small developers lack.
• What they did: The framework includes a three-layer performance model, multi-source ground-truth construction with evidence-weighting, a six-category disposition taxonomy, and a phased pathway, demonstrated on Helena Bioinformatics' platform with three cohorts.
• The result: This structured human-in-the-loop protocol offers a non-proprietary, adaptable methodology for early-stage performance assessment, explicitly acknowledging its limitations and avoiding claims of validation or superiority.