Negative Controls for Brain-Tumor Microdomain Claims

Authors

  • Walter Gardner Author
  • Leonard Knight Author
  • Stanley Harper Author

Abstract

The boundary between useful assistance and an unsupported claim becomes visible when co-location mistaken for a functional interaction. The article develops a research agenda through critical comparison of published evidence. The focal evidence concerns why autoimmune activity can persist or recur when conventional inflammation suppression does not durably change underlying cellular and tissue states. A mechanism gains support when a test could reasonably have contradicted it and did not. Co-occurrence and attractive diagrams are weaker when many alternatives predict the same observations. Prespecify distinguishing outcomes and appropriate negative controls. Interpret unexpected results rather than redefining the mechanism after every observation. The proposed comparison concerns spatial associations, glial cells, sampling conditions; it would test whether the specific failure scenario is detected without erasing legitimate uncertainty. An integrated account should preserve the distinction between tissue contexts, clinical endpoints, and mechanistic claims and any stronger downstream decision.

References

Yu, Xiao, et al. “Resetting Immunometabolic Set Points in Autoimmune Disease.” Journal of Translational Autoimmunity, vol. 13, 2026, https://doi.org/10.1016/j.jtauto.2026.100391.

Liu, Sunjiang, et al. “Machine Learning–Based Prediction of Early Visual Field Nonrecovery after Endoscopic Surgery for Nonfunctioning Pituitary Adenomas with Clinically Accessible Biomarkers: A Dual-Center Study.” International Journal of Clinical Practice, vol. 2026, no. 1, 2026, https://doi.org/10.1155/ijcp/2504255.

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.

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.

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.

Downloads

Published

2026-10-14

Issue

Section

Reviews and Perspectives