StochStack

prototype · diffusion mock

Ophthalmology Digital Twin · Diffusion Synthetic Control (Mock)

Mock diffusion pipeline for synthetic control arm generation: latent noise -> denoising steps -> calibrated patient-level output.

Setup

Endpoint: BCVA change at Week 24 (ETDRS letters) · Historical pool: AMD-118, AMD-122, AMD-OBS-19 (680)

Diffusion Run

seed: 9612

Denoising convergence curve

Observed control N: 120
Borrowed ESS: 65
Estimated control reduction: 29.8%

Observed vs Synthetic

BCVA change: observed 2.10 ± 7.40 | synthetic 2.36 ± 3.87 ·Δ +0.26
CST change: observed -32.0 ± 58.0 | synthetic -34.8 ± 32.4 ·Δ -2.8

Mock method note

This is a product mock: generated patients are simulated with a deterministic pseudo-random denoising loop, then calibrated to scenario-level control moments.

Synthetic Patient Samples

IDAgeBase BCVABase CSTDuration (y)BCVA ΔCST ΔWeight
SYN-00170.260.24054.2-1.5-620.79
SYN-00282.164.63713.712.2-250.87
SYN-00380.451.13994.74.9-290.79
SYN-00474.452.33843.76.5-500.79
SYN-00577.359.84893.40.6-120.82
SYN-00672.959.64045.16-840.82
SYN-00770.946.43746.2-0.5-450.78
SYN-00872.753.92923.14.1-510.79
SYN-00969.753.34014.90.5-650.83
SYN-01071.854.63893.45.5-870.84
SYN-01175.948.43884.2-0.6140.83
SYN-01269.760.93993.9-1.8-80.84

update log

Prototype Change Log

  1. 2026-03-05 · v0.1.0

    Ophthalmology Diffusion Twin (Mock) Launch

    • - Added a one-page mock prototype for synthetic control arm generation with diffusion-style denoising loop.
    • - Included ophthalmology scenarios (nAMD, DME, GA) with configurable cohort size, denoising steps, and calibration strength.
    • - Added side-by-side observed-vs-synthetic metrics and patient-level synthetic sample table for review.