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Updated: Apr 1, 2026

Assessment and Evaluation of the High Risk Neonate: The NICU Network Neurobehavioral Scale
Published on: August 25, 2014
When has enough evidence accumulated to change neonatal practice?
Elizabeth E Foglia1, Sara B DeMauro1, Kevin Dysart1
1The Children's Hospital of Philadelphia and The University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Determining adequate evidence from randomized clinical trials (RCTs) for changing neonatal medicine is crucial. This study examines cases of insufficient, excessive, and appropriate RCT evidence to suggest principles for standardizing therapies.
Area of Science:
- Neonatal medicine
- Clinical trial methodology
- Evidence-based practice
Background:
- Randomized clinical trials (RCTs) are the gold standard for evaluating therapeutic interventions.
- The optimal amount of evidence from RCTs needed to alter clinical practice remains unclear.
- Historical examples in neonatal medicine show therapies adopted with insufficient evidence or subjected to excessive testing.
Purpose of the Study:
- To analyze scenarios of insufficient, excessive, and appropriate evidence from RCTs in neonatal medicine.
- To propose principles for determining when sufficient evidence supports adopting a therapy into standard practice.
Main Methods:
- Case history analysis of therapeutic interventions in neonatal medicine.
- Review of evidence accumulation and clinical practice changes.
- Development of principles based on historical case studies.
Main Results:
- Identified distinct patterns of RCT application: insufficient evidence, excessive replication, and optimal evidence accumulation.
- Highlighted specific examples within neonatal medicine illustrating these patterns.
- Established a framework for evaluating evidence sufficiency.
Conclusions:
- The right amount of evidence is critical for effective and ethical implementation of new therapies.
- Principles derived from these case studies can guide decisions on adopting therapies into standard neonatal care.
- Avoiding both under-testing and over-testing of interventions ensures optimal patient outcomes.
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