Factors Influencing Optical Coherence Tomography Peripapillary Choroidal Thickness: A Multicenter Study
Bibliographic record
Abstract
Purpose: To quantify peripapillary choroidal thickness (PCT) and the factors that influence it in healthy participants who represent the racial and ethnic composition of the U.S. population. Methods: A total of 362 healthy participants underwent optical coherence tomography (OCT) enhanced depth imaging of the optic nerve head with a 24 radial B-scan pattern aligned to the fovea to Bruch's membrane opening axis. Bruch's membrane, anterior scleral canal opening (ASCO), and the anterior scleral surface were manually segmented. PCT was measured at 100, 300, 500, 700, 900, and 1100 μm from the ASCO globally and within 12 clock-hour sectors. The effects of age, axial length, intraocular pressure, ethnicity, sex, sector, and ASCO area on PCT were assessed by ANOVA and univariable and multivariable regressions. Results: Globally, PCT was thicker further from the ASCO border and thinner with older age, longer axial length, larger ASCO area, European descent, and female sex. Among these effectors, age and axial length explained the greatest proportion of variance. The rate of age-related decline increased further from the ASCO border. Sectorally, the inferior-temporal sectors were thinnest (10.7%-20.0% thinner than the thickest sector) and demonstrated a higher rate of age-related loss (from 15.6% to 20.7% faster) at each ASCO distance. Conclusions: In healthy eyes, PCT was thinnest in the inferior temporal sectors and thinner PCT was associated with older age, European descent, longer axial length, larger ASCO area, and female sex. Among these associations, age had the strongest influence, and its effect was greatest within the inferior temporal sectors.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".