Real:every West Bengal and India figure, each linked to its source; the clinical rules (KDIGO, ADA, Lipid Association of India, ATA / ETA, AASLD), applied exactly as written in each condition's rule table; and the code, which reads one row per reported lab parameter, the shape of the planned AKTIV to Neon lab_results view.
Simulated: every lab count, percentage and name. The cohort is generated to look like the AKTIV mirror once result values are synced: bills with a monthly PATIENT_KEY, names and phone numbers as a busy front desk types them, and hidden states for diabetes, kidney disease, cholesterol, thyroid and fatty liver that improve while people keep coming and slip while they are away. Its prevalences are set to look like a referral lab, not measured, so none of them should be quoted as findings from Anubhav Life Care.
Linking. The same resolver as the diabetes page: mobile number first, then a Bengali-aware fuzzy name match with sex and birth year. On this all-tests cohort it keeps 89.5% of joins correct and recovers 84.4% of true repeat visits, scored against the generator's hidden person id.
What a lab cannot see.Only tests done at the lab. Someone “lost” here may be testing elsewhere, have moved or have died, which is why the re-engagement queue asks first where they test now. Prescriptions, symptoms and clinic visits are not in the mirror. Scoring weights are a starting point for the clinical team, not a validated model.
When the mirror carries results, the same build produces each lab figure from real data: people on each pathway, loss to follow-up at 12 months, how many calls merging saves, and the checks nobody ordered. Those, set against the West Bengal column above, are the numbers to report.