GearCheck Pipeline: From Upload to Report in 6 Steps

First thing you do is upload your blood work. GearCheck takes three input types: a PDF directly from the lab, a photo or screenshot of your printed results, or manual entry if you have the numbers on paper.

During upload, GearCheck runs an immediate preflight check: it detects the file format, attempts text extraction, and runs a quick regex scan to count how many markers it can detect. If the panel looks too sparse (fewer than 10 markers found), it warns you before proceeding.

Estimated time: 2-3 seconds

Once the raw text is in hand, the extraction engine gets to work. It runs as a hybrid system, with two layers working in parallel to maximize coverage.

The result is a structured set of marker objects, each with a canonical name, value, unit, and reference range. Marker names are normalized to the GearCheck vocabulary, so "Testosteron," "Testosterone, Total," and "Total Testosterone" all map to the same canonical marker. That normalization is critical: without it, your results would be scattered across duplicates.

Units can be a source of confusion. European labs report in mmol/L, US labs in mg/dL. The extraction engine converts everything to canonical units internally, so your report is consistent regardless of where you tested.

The estimated time is 30 to 60 seconds, and this represents the bulk of pipeline time.

Every extracted marker is compared against GearCheck's athletic-adjusted reference bands, not the generic lab ranges printed on your report. These bands are calibrated specifically for strength athletes and, where applicable, AAS users.

Each marker gets a status: Optimal, Suboptimal, Monitor, Review, Context, or Descriptive. Your reference bands shift with your profile, adjusting for sex, age, training status, and whether you've flagged AAS use. Markers that fall outside the athletic range get pulled aside for deeper analysis in the next step.

Most generic lab analyzers stop right at this step. GearCheck doesn't. It keeps going, carrying the analysis further than the standard tools ever do.

GearCheck doesn't just read your numbers like a generic lab analyzer. The Contextual Interpretation Engine runs 20+ rules built around how AAS, training intensity, and body composition actually move your markers. Every rule is a medical logic pattern pulled from sports medicine literature.

A few key patterns stand out to the engine.

• When your testosterone sits high and your liver markers stay within normal range, that's not a red flag. That's the treatment working exactly as designed. You're not looking at some hidden pathology or a warning sign that something's about to go sideways. You're looking at the intended effect of the protocol doing its job. The liver values tell you the body is handling the load, and the testosterone tells you the therapy is effective. Both together mean you're in the sweet spot.
• Here's a pattern you'll see in bigger guys: low eGFR, high creatinine, but a normal Cystatin C. That combination points to muscle mass, not kidney disease. Creatinine is a byproduct of muscle breakdown, so the more tissue you carry, the higher your creatinine runs, and that drags eGFR down with it. Cystatin C doesn't care about muscle at all, so when that comes back normal, your kidneys are filtering exactly like they should. The labs look alarming on paper, but the story is simple: you're just carrying a lot of muscle, and that's not a problem, that's a win.
• When you see elevated AST and ALT alongside a high CK but a normal GGT, that pattern points to muscle leak, not hepatotoxicity. The CK is the key differentiator here, because muscle breakdown releases all three enzymes, while the liver stays quiet. If GGT were also climbing, you would be looking at a hepatic issue, but with CK elevated and GGT normal, the source is skeletal muscle, not the liver.
• Low HDL paired with high ApoB is the classic AAS-typical pattern, not a sign of genetic dyslipidemia. When you see that combination on a panel, it points straight at exogenous androgen use rather than an inherited lipid problem.
• Raised hematocrit with normal platelets and normal blood pressure, and the guy's doing regular cardio on top of that, is an expected pattern. You track it, you don't flag it.

Each rule can move a marker from Review down to Context, or from Optimal up to Monitor, depending on the full picture. Context alone can flip a marker from alarming to expected in milliseconds.

Once all markers are analyzed and contextualized, the assembly engine builds your complete report. That's the moment individual data points become a coherent story.

The assembly engine also groups findings by organ system. Flag multiple liver markers at once and the penalty multiplies, since concurrent signals mean more than isolated ones. That's how you end up with a risk assessment that tracks real physiology.

Everything from the previous steps comes together here, assembled into a report built to be read and acted on, not just skimmed.

Every report also includes a comparison to your previous results, so you can see trends at a glance. A single report shows where you are right now, while a series of reports reveals where you're headed.