Categorize
Reading the Patient, Not the Number; The Five C's: A Framework for Clinical Interpretation
Why laboratory values should be organized into biological systems before they are interpreted.
One of the biggest mistakes we make when reviewing laboratory data is assuming that every biomarker deserves its own interpretation.
It doesn’t.
In fact, interpreting laboratory values one at a time is one of the fastest ways to misunderstand what the biology is actually telling us.
This is partly how most of us are taught to review blood work. We scan the report from top to bottom, stopping whenever we encounter an abnormal result. We mentally note that the CRP is elevated, the albumin is low, the lymphocyte count has declined, or the glucose is slightly above the reference range. Each abnormality becomes its own clinical problem to explain.
The difficulty is that human physiology doesn’t work that way.
The body does not function as a collection of independent laboratory values. It functions as an integrated biological system, where inflammation influences immunity, immunity influences metabolism, metabolism affects endocrine signaling, nutrition affects tissue repair, and every system continuously communicates with the others.
Laboratory testing simply gives us individual measurements of that larger conversation.
If we interpret each measurement independently, we risk losing the conversation altogether.
From numbers to biology
Before I interpret a single laboratory value, I organize the data into biological systems.
This may sound like a small step, but it fundamentally changes how I think.
Rather than seeing thirty individual biomarkers, I begin to see six or seven biological processes that are either functioning well or struggling.
For example, inflammatory markers naturally belong together. CRP, ESR, ferritin, albumin, fibrinogen, platelet count, and even neutrophil predominance all provide different perspectives on the body’s inflammatory state. None of them tells the whole story, but together they begin to describe whether inflammation is acute, chronic, resolving, or progressively worsening.
The same is true for immune function. A white blood cell count, by itself, provides relatively little information. However, when viewed alongside neutrophils, lymphocytes, monocytes, the neutrophil-to-lymphocyte ratio, eosinophils, and, when available, immune phenotyping or flow cytometry, a much more meaningful picture begins to emerge. We can start asking whether the immune system appears activated, suppressed, recovering, redistributing, or responding appropriately to treatment.
The same principle applies across virtually every physiological system.
Markers of metabolic health belong together. Nutritional markers belong together. Liver function, kidney function, bone marrow recovery, coagulation, endocrine physiology, and tumor biology should each be viewed as systems rather than isolated measurements.
The goal is not to organize laboratory values for the sake of organization.
The goal is to reconstruct the biology they represent.
Biology doesn’t read laboratory reports.
This is an important distinction.
Patients do not experience CRP, they experience inflammation.
They do not experience albumin, hey experience nutritional reserve, healing capacity, and physiological resilience.
Laboratory medicine gives us measurable surrogates for biological processes. Our responsibility as clinicians is to translate those measurements back into physiology.
That shift changes everything.
Instead of asking, “Why is the ferritin elevated?” we begin asking, “What biological process could explain this inflammatory pattern?” The question becomes biological rather than numerical.
Every system influences another.
One reason categorization is so important is because biological systems rarely operate independently. Cancer itself interacts continuously with all of these systems while treatment adds another layer of complexity.
This is why interpreting one laboratory value in isolation often produces incomplete—or sometimes misleading—conclusions.
An elevated ferritin, for example, may initially suggest altered iron metabolism. However, when reviewed alongside CRP, ESR, albumin, fibrinogen, and the patient’s clinical history, it may become clear that the more relevant biological process is systemic inflammation.
Likewise, a mildly elevated glucose has a very different clinical significance when fasting insulin, HbA1c, triglycerides, body composition, corticosteroid exposure, and treatment timing are considered together.
The laboratory values have not changed but the biological interpretation has.
Categorization creates a map.
I often think of this step as building a map before planning the journey.
Without a map, every abnormal result competes equally for attention.
With a map, priorities begin to emerge.
We can identify which biological systems appear relatively stable, which are compensating, and which require closer attention. We begin recognizing whether the dominant story is inflammation, immune suppression, metabolic dysfunction, nutritional decline, treatment toxicity, recovery, or progression.
Only after that map has been built do I begin looking for the relationships between systems.
That is where the biology starts telling its story.
Because once the biology has been organized, it is no longer a collection of laboratory values. It becomes a network of relationships waiting to be understood.