Age Acceleration in a Working-Population Risk Model
摘要
The boundary between useful assistance and an unsupported claim becomes visible when a cross-sectional interaction described as accelerated causal aging. This literature-based conceptual analysis does not report a new experiment. The starting source uses sex-stratified cross-sectional modeling, longitudinal generalized estimating equations, and Cox-model nomograms using baseline high-frequency audiometry. An interaction is defined on a scale. Additive and multiplicative contrasts answer different questions and can disagree without either being a computational error. State the scale and reference categories explicitly. Compare effect modification under alternative defensible scales and avoid interpreting a fitted product term as a biological mechanism. The proposed comparison concerns phenotypic age, hearing status, occupation; it would test whether the specific failure scenario is detected without erasing legitimate uncertainty. An integrated account should preserve the distinction between noise-exposed workers and clinical follow-up and any stronger downstream decision.
参考
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.
Liu, Shunqi, et al. “A New Playing Method of the Guessing Football Lottery.” IOP Conference Series: Materials Science and Engineering, vol. 790, no. 1, 2020, https://doi.org/10.1088/1757-899x/790/1/012100.
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.
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.
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.