Minimizing Pupil Size Dependence in Flicker ERG Using Stiles–Crawford Compensation
Bibliographic record
Abstract
Purpose: This study determined the impact of the Stiles-Crawford effect (SCE) on electroretinograms (ERGs). Compensating for the SCE can improve the diagnostic reliability of ERGs by providing a stimulus minimally affected by pupil size. Methods: Flicker ERGs were recorded from 10 healthy subjects at 3-minute intervals over 21 minutes after mydriasis. The RETeval system adjusted retinal illuminance in real time based on pupil size measurements, using a Troland stimulus and preset SCE compensation factors (ρdevice) of 0, 0.05, 0.085, and 0.12 mm-2. Results: Larger pupil areas led to prolonged implicit times with ρdevice = 0 and 0.05 mm-2, whereas ρdevice = 0.12 mm-2 reduced implicit time. Amplitudes were lower with ρdevice = 0 mm-2 but increased with ρdevice = 0.085 and 0.12 mm-2. The values that minimized pupil size dependence were ρdevice = 0.086 mm-2 for the implicit time of the fundamental component of the ERG and ρdevice = 0.05 mm-2 for all other measures. Variability in ERGs based on pupil size is predicted to be ≤7% of the associated 95% reference interval for Troland stimuli over the range of nonmydriatic pupil sizes, compared to ≤43% for luminance stimuli over the range of mydriatic pupil sizes. Conclusions: Using Troland stimuli with ρdevice = 0.05 mm-2 for all cone-mediated ERGs would minimize the impact of pupil size, although the improvement would be modest for ERGs performed with Troland stimulation without SCE compensation on non-dilated subjects. Translational Relevance: Applying the appropriate SCE coefficient (ρdevice) enables more reliable ERG measurements, improving diagnostic accuracy despite pupil size variations in clinical settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".