Medication Histories, Feeding Histories, and Data Time
摘要
Changing exposure records, longitudinal decisions, documentation create distinct evidence obligations in maternal-infant evidence communication. This literature-based conceptual analysis does not report a new experiment. The focal evidence concerns how evidence concerning HIV, pregnancy, and infant feeding should be interpreted in ongoing clinical communication. Records collected at different times do not automatically describe the same decision state. Medication, exposure, symptoms, and evidence availability can change between observations. Inspect timestamp gaps and define permissible windows. Compare contemporaneous and stale combinations and report whether interpretation changes when alignment is tightened. The proposed comparison concerns changing exposure records, longitudinal decisions, documentation; it would test whether the specific failure scenario is detected without erasing legitimate uncertainty. Implications are restricted to the evidence roles relevant to maternal-infant evidence communication, leaving broader efficacy and deployment claims unresolved.
参考
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.
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.
Benjamini, Yoav, and Yosef Hochberg. “Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing.” Journal of the Royal Statistical Society: Series B, vol. 57, no. 1, 1995, pp. 289–300.
Rajkomar, Alvin, Jeffrey Dean, and Isaac Kohane. “Machine Learning in Medicine.” New England Journal of Medicine, vol. 380, 2019, pp. 1347–58.
Wiens, Jenna, et al. “Do No Harm: A Roadmap for Responsible Machine Learning for Health Care.” Nature Medicine, vol. 25, 2019, pp. 1337–40.