Pregnancy and Infant Feeding as a Longitudinal Evidence Problem

Authors

  • Troy Gardner Author
  • Dustin Rhodes Author
  • Travis Walsh Author

Abstract

Maternal care, feeding decisions, changing clinical status create distinct evidence obligations in maternal-infant evidence communication. A bounded review of the literature is used to formulate, rather than claim to complete, an evaluation. The starting source uses a JAMA correspondence contribution rather than a newly reported cohort or randomized intervention. Longitudinal decisions depend on what is known now and how that knowledge may change. A record of past status does not guarantee future status. Specify review intervals and triggers for reassessment. Audit stale records and distinguish general published knowledge from individualized current evidence. The proposed comparison concerns maternal care, feeding decisions, changing clinical status; 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.

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.

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.

Downloads

Published

2026-10-14

Issue

Section

Reviews and Perspectives