[Is the age of causality over?]

Area of Science:

• Medical research methodology
• Health services research

Background:

• Evidence-based Medicine (EBM) has established tools for assessing medical interventions over the past two decades.
• A core principle in EBM is maximizing protection against bias when grading study evidence.
• Controversies exist, often stemming from the prominence of randomized controlled trials versus observational studies.

Purpose of the Study:

• To emphasize the critical need for rigorous scientific criteria in evaluating medical interventions.
• To address the challenges posed by emerging trends like Big Data in medical research.
• To advocate for adherence to established validation principles amidst evolving data analysis paradigms.

Main Methods:

• The abstract discusses the principles and evolution of evidence grading in medical research.
• It highlights the ongoing debate regarding the hierarchy of evidence, contrasting randomized controlled trials with observational studies.
• It critiques the reliance on 'common sense' and the lack of explicit scientific criteria in some evaluation discussions.

Main Results:

• Rigorous application of basic scientific principles is essential for unbiased assessments in medicine.
• The current discourse around Big Data, characterized by Variety, Velocity, and Volume, lacks validation and has questionable logical foundations.
• There is a risk of moving away from established scientific foundations towards unverified methodologies.

Conclusions:

• The evaluation of medical interventions must strictly adhere to validation criteria, not just promises.
• The scientific community should uphold established principles, especially when confronted with new, unvalidated approaches like Big Data analysis.
• A call for reaffirming strict validation criteria in the assessment of all medical interventions is necessary.