Prospective Warnings with Incomplete Follow-Up
Abstract
In occupational hearing-risk assessment, a convincing output can still be invalid when those leaving noisy jobs disappearing from evaluation. This literature-based conceptual analysis does not report a new experiment. The primary literature contributes a bounded result: the accessible publication describes 7139 initially recruited noise-exposed subjects and longitudinal and external evaluation; baseline 3-kHz and 3–6-kHz measures contribute prognostic information. An absent observation has a process behind it. Silence, withdrawal, sensor failure, and unrecorded outcomes are not interchangeable blanks. Describe who can become missing and when. Compare complete-case conclusions with explicit alternative missing-data assumptions, and distinguish data recovery from unsupported imputation. The proposed comparison concerns worker turnover, follow-up audiometry, referral records; it would test whether the specific failure scenario is detected without erasing legitimate uncertainty. The original source's limits remain material: Limited follow-up, a smaller longitudinal sample, occupational selection, and untested prospective clinical impact constrain deployment claims.
References
Yu, Xiao, et al. “Prediction of Risk of Hearing Loss by Industry Noise from Cross-Sectional and Longitudinal Data.” Communications Medicine, vol. 6, 2026, https://doi.org/10.1038/s43856-026-01463-3.
Li, Yuanhao, et al. “BoostAPR: Boosting Automated Program Repair via Execution-Grounded Reinforcement Learning with Dual Reward Models.” arXiv, version 4, 18 Sept. 2026, https://arxiv.org/abs/2605.09134v4. Preprint.
Dai, Junjian, et al. “Interaction between Noise Exposure and Phenotypic Age Acceleration in Hearing Loss: An NHANES Study.” ENT Updates, vol. 16, no. 3, 2026, pp. 1–12, https://doi.org/10.54963/entu.v16i3.100576.
Sang, Yinghao. “Adaptive Quantization Strategies for Robust ML Inference under Distribution Shift.” Proceedings of the 2026 5th International Conference on Cyber Security, Artificial Intelligence and Digital Economy, ACM, 2026, pp. 362–68, https://doi.org/10.1145/3819836.3819892.
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.
Rusu, Andrei A., et al. “Policy Distillation.” arXiv, 2015, arXiv:1511.06295.
Schulman, John, et al. “Proximal Policy Optimization Algorithms.” arXiv, 2017, arXiv:1707.06347.
Tishby, Naftali, Fernando C. Pereira, and William Bialek. “The Information Bottleneck Method.” Proceedings of the 37th Annual Allerton Conference on Communication, Control, and Computing, 1999, pp. 368–77.
Alemi, Alexander A., et al. “Deep Variational Information Bottleneck.” International Conference on Learning Representations, 2017.
National Academies of Sciences, Engineering, and Medicine. Reproducibility and Replicability in Science. National Academies Press, 2019.