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From drug lists to decisions: Why Oncology needs better patient selection guidance
Clinicians need to know which patients should receive each therapy and why.
May 2
Why AESIs matter more than ever in Oncology
A real shift in how clinicians think about risk, monitoring and treatment selection.
Apr 27
Clinical trials vs. real patients: Why clinicians need decision tools for the real world
Trials are designed around carefully selected patients, but real world patients rarely match this criteria.
Apr 24
Why oncology decision tools need to be specific (and less fluffy)
Clinicians need precise, actionable information to make the best decisions for patients.
Apr 23
How do I know this is up to date? Why versioning and trust matter in oncology decision tools
Living Algorithms combine updates and transparency, helping you act with confidence.
Apr 22
Visual thinking in Oncology: Why flowcharts, risk scoring and survival curves matter
Flowcharts and OS curves make complex decisions easier to understand and communicate.
Apr 11
How oncologists actually use algorithms (and why most tools get it wrong)
Most oncology tools are organized around drugs or documents, but this isn't the right approach.
Apr 11
Dosing, side effects and real-world modifications: What the guidelines don't tell you
Bringing dosing, side effects, monitoring and adjustments together can help you make decisions.
Apr 11
Data-free zones in Oncology: What to do when the evidence doesn't exist
Data-free zones are part of real-world practice, but Living Algorithms can help you...
Apr 11
Risk stratification in Oncology: Why "low, medium, high" isn't enough
Oncologists need precise scores, definitions and context for risk stratification.
Apr 11
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