Noise-Resistant Retrieval for Patient-Reported Sleep Research
Abstract
A central question for subjective and objective sleep evidence is not simply whether an output is accurate, but whether it survives scrutiny of retrieval relevance. The article develops a research agenda through critical comparison of published evidence. The focal evidence concerns whether subjective sleep gains in empty nose syndrome are sufficient to support claims about objectively improved sleep. Retrieval relevance is conditional on a claim and its intended population. A passage can be topically similar yet fail to support the exact inference. Construct source sets with related but ineligible papers and contradictory evidence. Measure whether the generated claim stays within the support actually retrieved. The proposed comparison concerns symptom reports, objective tests, source eligibility; it would test whether the specific failure scenario is detected without erasing legitimate uncertainty. Implications are restricted to the evidence roles relevant to subjective and objective sleep evidence, leaving broader efficacy and deployment claims unresolved.
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.
Sang, Yinghao. “AutoCrit: A Meta-Reasoning Framework for Self-Critique and Iterative Error Correction in LLM Chains-of-Thought.” 2025 6th International Conference on Machine Learning and Computer Application (ICMLCA), IEEE, 2025, pp. 1177–80, https://doi.org/10.1109/icmlca66850.2025.11336788.
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.
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.