Bibliographic record
Abstract
SIGNIFICANCE: This study provides a new method to measure the refractive index of crystalline lens in the human eye in vivo . PURPOSE: Accessing the refractive index of crystalline lenses in the human eye in vivo has long been a challenge. This study aimed to measure the refractive index of a lens in vivo using an anterior segment optical coherence tomography (AS-OCT) system combined with a Scheimpflug imaging system. METHOD: A ray-traceable Scheimpflug imaging was developed and integrated into an AS-OCT system. A theoretical study revealed that the combination of these two systems provides a unique solution for simultaneously measuring the refractive index and the thickness of the lens. The average lens refractive index along the ray pathway ( Nav ) and lens shape were measured for seven subjects. RESULTS: The lens Nav along the central ray varies from 1.383 to 1.419 between subjects. The lens refractive index function across the lens diameter varies from subject to subject. The lens Nav increases for accommodated eyes. The thicknesses and profiles of the lenses are also determined. CONCLUSIONS: The lens refractive index varies substantially from individual eye to individual eye, not only along the central ray pathway but also for the lens refractive index function across the lens diameter. Ray-traceable Scheimpflug imaging-equipped AS-OCT is useful for testing the refractive index of lenses in the human eye in vivo .
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".