ReviewResuscitation2023
Self-fulfilling prophecies and machine learning in resuscitation science.
Review in Resuscitation, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Part 3: Ethics: 2025 American Heart Association Guidelines for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care.Circulation · 2025Guideline
- Bias and Fairness Across the Healthcare AI Lifecycle: A Clinician-Oriented Review.Balkan medical journal · 2026Review
- Cardiovascular Disease, Sleep-Disordered Breathing, and Artificial Intelligence: From Neutral Trials to Precision Sleep Cardiology.Reviews in cardiovascular medicine · 2026Review
- Neurological outcomes after out-of-hospital cardiac arrest due to aneurysmal subarachnoid hemorrhage: a multicenter cohort study.Critical care (London, England) · 2026Article
- Multimodal contrastive prognostication framework for early neurological outcome prediction in post-cardiac arrest patients.Scientific reports · 2026Article
- Improving sepsis care with Artificial Intelligence. What would Illich say?Critical care science · 2026Article
- Humans versus machine learning models: improving the outcome of trauma patients.The Lancet regional health. Europe · 2025Article
- Estimating bias from withdrawal of life sustaining treatment in clinical trials.Critical care (London, England) · 2025Review
- Monro-Kellie 4.0: moving from intracranial pressure to intracranial dynamics.Critical care (London, England) · 2025Review
- Recovery Potential in Patients After Cardiac Arrest Who Die After Limitations or Withdrawal of Life Support.JAMA network open · 2025Article
- Traumatic Brain Injury as an Independent Predictor of Futility in the Early Resuscitation of Patients in Hemorrhagic Shock.Journal of clinical medicine · 2024Review
- Prognosticating the outcome of intensive care in older patients-a narrative review.Annals of intensive care · 2024Review
- Disorders of Consciousness Rehabilitation: Ethical Dimensions and Epistemic Dilemmas.Physical medicine and rehabilitation clinics of North America · 2024Review
- Simple approach to quantify hypoxic-ischemic brain injury severity from computed tomography imaging files after cardiac arrest.Resuscitation · 2024Article
- AI and machine learning in resuscitation: Ongoing research, new concepts, and key challenges.Resuscitation plus · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
Abstract
introductionGrowth of machine learning (ML) in healthcare has increased potential for observational data to guide clinical practice systematically. This can create self-fulfilling prophecies (SFPs), which arise when prediction of an outcome increases the chance that the outcome occurs.
methodsWe performed a scoping review, searching PubMed and ArXiv using terms related to machine learning, algorithmic fairness and bias. We reviewed results and selected manuscripts for inclusion based on expert opinion of well-designed or key studies and review articles. We summarized these articles to explore how use of ML can create, perpetuate or compound SFPs, and offer recommendations to mitigate these risks.
resultsWe identify-four key mechanisms through which SFPs may be reproduced or compounded by ML. First, imperfect human beliefs and behavior may be encoded as SFPs when treatment decisions are not accounted for. Since patient outcomes are influenced by a myriad of clinical actions, many of which are not collected in data, this is common. Second, human-machine interaction may compound SFPs through a cycle of mutual reinforcement. Third, ML may introduce new SFPs stemming from incorrect predictions. Finally, historically correct clinical choices may become SFPs in the face of medical progress.
conclusionThere is a need for broad recognition of SFPs as ML is increasingly applied in resuscitation science and across medicine. Acknowledging this challenge is crucial to inform research and practice that can transform ML from a tool that risks obfuscating and compounding SFPs into one that sheds light on and mitigates SFPs.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.