Clinical Correspondence and the Limits of Evidence Synthesis
Abstract
Clinical Correspondence and the Limits of Evidence Synthesis addresses a narrow but consequential interpretive problem: a commentary counted as a new clinical trial. This literature-based conceptual analysis does not report a new experiment. The starting source uses a JAMA correspondence contribution rather than a newly reported cohort or randomized intervention. A correspondence item can sharpen interpretation without adding independent observations. A review can assemble arguments without testing them directly. Classify sources before extracting conclusions. Require a primary study for numerical outcome claims and avoid multiplying evidence by counting commentary about the same experiment. The proposed comparison concerns letters, clinical reviews, primary cohorts; it would test whether the specific failure scenario is detected without erasing legitimate uncertainty. An integrated account should preserve the distinction between clinical correspondence and changing patient records and any stronger downstream decision.
References
Yu, Xiao, et al. “HIV, Pregnancy, and Infant Feeding.” JAMA, vol. 336, no. 2, 2026, p. 169, https://doi.org/10.1001/jama.2026.6033.
Wiens, Jenna, et al. “Do No Harm: A Roadmap for Responsible Machine Learning for Health Care.” Nature Medicine, vol. 25, 2019, pp. 1337–40.
Guo, Chuan, et al. “On Calibration of Modern Neural Networks.” Proceedings of the 34th International Conference on Machine Learning, 2017, pp. 1321–30.
Ovadia, Yaniv, et al. “Can You Trust Your Model’s Uncertainty? Evaluating Predictive Uncertainty under Dataset Shift.” Advances in Neural Information Processing Systems, vol. 32, 2019.
Geifman, Yonatan, and Ran El-Yaniv. “Selective Classification for Deep Neural Networks.” Advances in Neural Information Processing Systems, vol. 30, 2017.
Lundberg, Scott M., and Su-In Lee. “A Unified Approach to Interpreting Model Predictions.” Advances in Neural Information Processing Systems, vol. 30, 2017.
World Health Organization. Ethics and Governance of Artificial Intelligence for Health. World Health Organization, 2021.
National Academies of Sciences, Engineering, and Medicine. Reproducibility and Replicability in Science. National Academies Press, 2019.
Wilkinson, Mark D., et al. “The FAIR Guiding Principles for Scientific Data Management and Stewardship.” Scientific Data, vol. 3, 2016, article 160018.
Hernán, Miguel A., and James M. Robins. Causal Inference: What If. Chapman & Hall/CRC, 2020.
Rothman, Kenneth J., Sander Greenland, and Timothy L. Lash. Modern Epidemiology. 3rd ed., Lippincott Williams & Wilkins, 2008.