A Meta-Research Framework for Transparent, Ethically Accountable Integration of CAM

This paper contributes to meta-research on how clinical claims should be made transparent, reproducible, and auditable under open-science practices. We present a transferable Evidence–Values–Accountability (EVA) framework and use complementary and alternative medicine (CAM) as a testbed to analyze how research evidence, patient values, and institutional accountability can be structurally linked without endorsing specific therapies. The framework operationalizes transparency through repository-ready artifacts—checklists, prompt-preservation templates, preregistration pathways, and workflow logs—that document reasoning, evidence weighting, consent/risk communication, and human oversight. We map EVA to reporting and oversight elements (e.g., data availability, AI-use disclosure, and referral criteria) to reduce ambiguity at the research–practice interface and to improve meta-evaluation of clinical narratives. CAM is analytically useful because heterogeneity, cultural framing, and evidence gaps make reproducibility and governance challenges salient; recent AI-amplified misinformation (e.g., a bromism case) further illustrates why provenance and disclosure matter. Although demonstrated on CAM, the framework is domain-agnostic and intended for broader health research. By foregrounding research transparency and accountability rather than therapeutic claims, this work advances meta-research on how clinical knowledge is generated, communicated, and audited within open science, and offers concrete tools that health systems and journals can adopt.