This page explains exactly how FixFirst produces its output — from the moment you upload a report to the ranked list of priorities you see. It covers what AI does, what deterministic rules do, and where the clinical decisions come from.
A large language model (Claude, by Anthropic) reads the uploaded PDF or image and identifies every blood marker: name, value, unit, lab reference range, and any H/L flags printed by the lab. The AI handles the wide variation in lab report formats — Quest Diagnostics, LabCorp, NHS, and private labs all lay out results differently. The AI does not make any clinical judgement at this stage. It produces structured data only.
The extracted values are passed through a deterministic rules engine that applies evidence-based threshold overrides. This step handles two important problems: (1) lab reference ranges vary between labs and can be wider than current clinical guidelines recommend; (2) some markers require sex- or age-adjusted interpretation that the raw lab range does not reflect. For example, a TSH of 3.8 mIU/L may be within a lab's printed range but warrant borderline classification based on ATA guidelines. All threshold decisions are hardcoded, traceable to a specific guideline, and medically reviewed.
Each non-optimal marker is scored across six dimensions: severity of deviation, clinical impact, actionability with lifestyle/supplement interventions, expected response time to intervention, questionnaire context (sex, age, medications, diet), and lab flag weighting. The three markers with the highest composite scores become the Top 3 priorities. Markers that rank 4th or lower are shown in a secondary list with graded status labels (Slightly High, High, etc.) but are not accompanied by full protocol recommendations — the intent is to direct attention, not to overwhelm.
Protocol items — diet, lifestyle, supplements, and expected response timelines — are drawn from a static database keyed to each marker's identity, severity tier, and direction (high or low). No AI generates these recommendations. Every item in the database was written with reference to published clinical guidelines and reviewed by Dr. Prahlad Rai Gupta. When a top-3 marker conflicts with the dietary advice for another top-3 marker (e.g., high iron and low calcium requiring different intake guidance), a cross-marker reconciliation step flags the conflict explicitly.
The clinical outputs of FixFirst — what is flagged, how it is ranked, and what is recommended — are fully deterministic and not subject to the hallucination or inconsistency risks of generative AI. The AI only touches the extraction step.
Each marker's threshold values are sourced from the primary clinical guideline most specific to that marker, then reviewed by Dr. Prahlad Rai Gupta. When guidelines disagree, the more conservative threshold is used and the conflict is documented in the codebase with a comment citing both sources.
The primary guideline families used:
An optional questionnaire captures biological sex, age, medications (specifically statins and thyroid medication), and dietary pattern (plant-based or omnivore). These answers adjust interpretation in specific, documented ways:
Context adjustments are applied at the threshold correction and ranking stages. They are not applied to the AI extraction step.
When two authoritative sources set different thresholds for the same marker, FixFirst uses the following decision order:
FixFirst tells you which markers are worth your attention first, based on their values relative to evidence-based thresholds. What you do with that information — including whether and how to discuss it with a doctor — is a decision that remains entirely yours.
Threshold values, ranking logic, and recommendation content are reviewed against their source guidelines on a quarterly basis. When a major guideline is updated — for example, a new edition of the ADA Standards of Care — the affected thresholds are reviewed and updated before the next content publication cycle.
Material changes to the ranking algorithm or threshold database are tested against a regression suite before deployment to ensure they do not silently change the output for previously consistent test cases.
Questions about the methodology or requests for the specific threshold values used for a given marker can be sent to fixfirstio@gmail.com.