AAOO

Probabilistic Spectacle Performance Framework

Paired Monte Carlo assessment of wearing-state and systematic-error consequences across gaze.

server-calculated
Live demonstration Apply a preset, retain the paired seed, then reveal the field.
Prescription and task
Right eye
Left eye
Position of wear
Random variation
Optical field and criteria
Systematic error
Simulation field
Expectation demonstration

Exploratory reference model. These values do not alter the optical Monte Carlo calculation.

Ready.
Run the server model to calculate the paired comparison.
Optical burden index—1 − mean Q*; not rejection probability
States crossing a criterion—integrated criteria
Baseline compatibility—at task gaze
Error compatibility—at task gaze
Signed difference—error minus baseline
Paired harm probability—Q* deterioration > ε

Optical consequence across gaze

The three-dimensional surface is the presentation view. Drag to rotate, scroll to zoom, or select the two-dimensional inspection view.

Run the server model to generate the surface.

Results will be calculated on the server.

Selected gaze cell

Click any map cell to inspect its paired distribution and optical-loss components.

Task gaze
Intended Q* median—5th–95th percentile
Error Q* median—5th–95th percentile
Median ΔQ*—5th–95th percentile
Compatibility—intended → error
Paired harm—ΔQ* below −ε

Mean normalised loss by component

Bars show the intended and error scenario means. The components are model terms relative to the selected limits.

Exploratory expectation overlay

A separate demonstration of how an unchanged performance state can become salient as the internal reference changes.

not clinically calibrated
previous expected
Current reference—
Expectation shortfall—
Salience state—
Nominal crossing time—

Interpretation boundary

The model-estimated optical burden index is 1 − mean(Q*). It must not be interpreted as the percentage of patients who will reject the spectacles.

The intended and error scenarios use common random states. Signed differences therefore estimate the consequence of the stated systematic change rather than independent Monte Carlo noise.