Preserving a decade of machine-learning validity across a ground-up refactoring: a five-tier automated validation of RCT-Reviewer, an independent modernization of RobotReviewer
· 2026-09-10 · 原文
DOI：10.64898/2026.09.08.26362546v1?rss=1预印本：medRxiv开放获取：绿色OA
Evidence-synthesis teams increasingly depend on machine-learning tools to automate risk-of-bias assessment, but these tools frequently rely on unrunnable, deprecated software stacks. Refactoring legacy tools into modern environments is essential for maintenance, yet introduces a critical risk: silently invalidating published performance metrics. Without rigorous validation, systematic reviewers cannot trust that modernized tools retain their predecessors' behavior. We present a five-tier validation framework applied to RCT-Reviewer, an independent ground-up modernization of the widely used RobotReviewer system. To prove preservation, we engineered a compatibility shim to execute the original 2017 code alongside the modernized tool using byte-identical weight files. The framework evaluated
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1. 人话版
Evidence-synthesis teams increasingly depend on machine-learning tools to automate risk-of-bias assessment, but these tools frequently rely on unrunnable, deprecated software stacks.
Refactoring legacy tools into modern environments is essential for maintenance, yet introduces a critical risk: silently invalidating published performance metrics.
2. 领域脉络
Without rigorous validation, systematic reviewers cannot trust that modernized tools retain their predecessors' behavior.
3. 机制拆解
We present a five-tier validation framework applied to RCT-Reviewer, an independent ground-up modernization of the widely used RobotReviewer system.
4. 证据与数字
To prove preservation, we engineered a compatibility shim to execute the original 2017 code alongside the modernized tool using byte-identical weight files.
5. 反例与边界
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6. 跨领域连接与意外收获
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7. 可复用方法
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8. 术语表
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