Correlated Error in Reviews of Immune Dysregulation

Authors

  • Dean Bishop Author
  • Philip Davidson Author
  • Martin Dawson Author

Abstract

In chronic pulmonary immune-dysregulation research, a convincing output can still be invalid when agreement between reviews counted as experimental replication. A bounded review of the literature is used to formulate, rather than claim to complete, an evaluation. The primary literature contributes a bounded result: the publication synthesizes low-grade inflammation, tissue remodeling, tolerance, and disease-specific pulmonary contexts. Repeated narratives can arise because reviews cite the same influential paper. Their agreement is then a property of the literature network as well as of the underlying phenomenon. Trace claims back to primary observations and identify overlapping evidence paths. Report where apparent consensus rests on few independent studies. The proposed comparison concerns review overlap, critique models, shared references; it would test whether the specific failure scenario is detected without erasing legitimate uncertainty. An integrated account should preserve the distinction between disease-specific biofilm and immune observations and any stronger downstream decision.

References

Yu, Xiao, et al. “Biofilm-Mediated Immune Dysregulation in Chronic Pulmonary Diseases: Mechanisms and Clinical Implications.” Frontiers in Microbiology, vol. 16, 2026, https://doi.org/10.3389/fmicb.2025.1736384.

Guo, Qian, et al. “Immune Niches in Brain Tumors: Glial–Immune Cell Interactions and Spatial Microdomains.” Acta Neuropathologica Communications, vol. 14, 2026, https://doi.org/10.1186/s40478-026-02373-0.

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.

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.

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.

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Published

2026-10-14

Issue

Section

Reviews and Perspectives