Pupil Scaling for the Estimation of Aberrations in Natural Pupils
Bibliographic record
Abstract
PURPOSE: This study aimed to validate the mathematical Zernike pupil size scaling from bigger pupils to smaller pupils, and vice versa, by comparing the estimates of the Zernike coefficients with corresponding clinical measurements obtained at different pupil sizes. METHODS: The i.Profiler Plus (Carl Zeiss Vision, Inc, USA) was used to obtain measures of wavefront aberrations for two pupil sizes (3 mm and the maximum natural pupil size) from the right eyes of 28 visually normal subjects (mean [±SD] age, 57 [±7] years) whose maximum pupil size was greater than or equal to 5 mm without pharmacological dilation. Zernike coefficients were estimated for a 3-mm pupil size scaling down from the measured data of the maximum natural pupil size and, similarly, for the maximum pupil size scaling up from the measured data of the 3-mm pupil. RESULTS: The differences between the estimated and measured values were not significantly different (repeated-measures analysis of variance; p > 0.05) over the range of pupil sizes examined, irrespective of whether the estimates were made by scaling up from a small pupil or scaling down from a large pupil. However, the difference between the measured and estimated coefficients was more variable and less systematic when scaling to a larger pupil size when compared with scaling to a smaller pupil size. CONCLUSIONS: Estimation of ocular wavefront aberration coefficients either scaling down from large to smaller pupils or scaling up from smaller to large pupils provides estimates that are not significantly different from clinically measured values. However, when scaling up to a larger pupil size, the estimates are more variable. These findings have implications for pupil scaling on an individual basis, such as in cases of refractive surgery or when using pupil scaling to examine a clinical cohort.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".