<p>Neuroprognostication after cardiac arrest should not be understood as a conventional diagnostic exercise aimed at achieving definitive early classification. Because prognostic judgments often influence decisions regarding withdrawal of life-sustaining therapy, they are shaped by ethical asymmetry, irreducible uncertainty, and the risk of self-fulfilling prophecy. This viewpoint argues that contemporary neuroprognostication is better conceptualized as a multimodal, longitudinal, and ethically constrained clinical strategy. Current guideline-based approaches appropriately prioritize near-zero false-positive rates for poor outcome prediction, accepting reduced sensitivity and leaving a substantial proportion of patients prognostically indeterminate. Such indeterminacy is best understood as a necessary safeguard rather than a methodological failure. The article also discusses the limited role of predictors of favorable recovery in decision-making, the constraints of static and AI-based predictive models, and the importance of structured care pathways that enable serial reassessment and interdisciplinary continuity. Future progress will depend not on eliminating uncertainty, but on managing it responsibly across diverse clinical and resource settings.</p>

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Neuroprognostication After Cardiac Arrest: From Test-Centered Prediction to Ethically Constrained, Longitudinal Decision-Making

  • Javier Andrés Mora-Arteaga

摘要

Neuroprognostication after cardiac arrest should not be understood as a conventional diagnostic exercise aimed at achieving definitive early classification. Because prognostic judgments often influence decisions regarding withdrawal of life-sustaining therapy, they are shaped by ethical asymmetry, irreducible uncertainty, and the risk of self-fulfilling prophecy. This viewpoint argues that contemporary neuroprognostication is better conceptualized as a multimodal, longitudinal, and ethically constrained clinical strategy. Current guideline-based approaches appropriately prioritize near-zero false-positive rates for poor outcome prediction, accepting reduced sensitivity and leaving a substantial proportion of patients prognostically indeterminate. Such indeterminacy is best understood as a necessary safeguard rather than a methodological failure. The article also discusses the limited role of predictors of favorable recovery in decision-making, the constraints of static and AI-based predictive models, and the importance of structured care pathways that enable serial reassessment and interdisciplinary continuity. Future progress will depend not on eliminating uncertainty, but on managing it responsibly across diverse clinical and resource settings.