Patient Experience and Measurement Discordance

Authors

  • Stanley Wheeler Author
  • Norman Palmer Author
  • Gordon Harper Author

Abstract

Subjective benefit, physiological signals, treatment goals create distinct evidence obligations in subjective and objective sleep evidence. A bounded review of the literature is used to formulate, rather than claim to complete, an evaluation. The focal evidence concerns whether subjective sleep gains in empty nose syndrome are sufficient to support claims about objectively improved sleep. Different outcomes can matter simultaneously. A patient-reported improvement and an unchanged physiological measure can each be real and relevant. Prespecify outcome roles and inspect discordance. Preserve patient priorities while withholding unsupported physiological or efficacy conclusions. The proposed comparison concerns subjective benefit, physiological signals, treatment goals; it would test whether the specific failure scenario is detected without erasing legitimate uncertainty. The original source's limits remain material: Its argument cannot establish the efficacy or inefficacy of a particular treatment, and measurement discordance does not invalidate patient-reported experience.

References

Yu, Xiao, et al. “Subjective Sleep Gains in Empty Nose Syndrome: A Cautionary Note on the Need for Objective Validation.” International Forum of Allergy & Rhinology, vol. 15, no. 7, 2025, pp. 761–62, https://doi.org/10.1002/alr.23631.

Ding, Siqi, et al. “Multifunctional Hydrogel Sensors with Dynamic Covalent Networks for Machine Learning-Assisted Parkinson's Disease Diagnosis and Encrypted Human-Computer Interaction.” Materials Today Bio, vol. 35, 2025, https://doi.org/10.1016/j.mtbio.2025.102524.

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.

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.

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Published

2026-10-14

Issue

Section

Reviews and Perspectives