Repairing Audiometry Pipelines without Rewriting Clinical Meaning
Abstract
Test fixtures, frequency units, clinical labels create distinct evidence obligations in occupational hearing-risk assessment. 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. A computational specification is a translation of a substantive question. Errors can occur in that translation even when every implementation step is correct. Build examples with known substantive answers, including boundary cases. Review diffs against a written endpoint dictionary and require approval for meaning-changing patches. The proposed comparison concerns test fixtures, frequency units, clinical labels; 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, Y., Wang, H., Shang, X., Tang, X., Cao, Y., & Chen, X. (2026). BoostAPR: Boosting Automated Program Repair via Execution-Grounded Reinforcement Learning with Dual Reward Models. arXiv preprint arXiv:2605.09134.
- Tan, Wei, et al. “Evo-CuRL: Curriculum-Aware Reinforcement Learning over Code Lineage Graphs for Software Engineering Reasoning.” Proceedings of the 2026 International Conference on Multimedia Retrieval, ACM, 2026, pp. 1327–35, https://doi.org/10.1145/3805622.3810701.
- 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.
- 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.
- 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.