Optical Coherence Tomography Based Choroidal Thickness and Its Determinants in Healthy Saudi Population: A Cross-Sectional Study
Bibliographic record
Abstract
Purpose To study choroidal thickness (CT) and its determinants based on optical coherence tomography (OCT) in the healthy adult Saudi population. Materials and methods This cross-sectional study was conducted in 2021 at a tertiary eye hospital in Saudi Arabia. The autorefractor-based refractive status (spherical equivalent) of each eye was documented. CT was measured from the enhanced depth OCT images at the fovea to the 1500 µm nasal and temporal to the fovea. CT was defined as the distance from a hyper-reflective line representing retinal pigment epithelium (RPE)-Bruch's membrane to the choroid-scleral junction. The CT was correlated with demographic and other variables. Results The study sample included 288 eyes of 144 participants (mean age 31.5±8.3 years; males 94, 65.3%). Emmetropia, myopia, and hypermetropic spherical equivalent were noted in 53 (18.4%), 152 (52.5%), and 83 (28.8%) eyes, respectively. The mean sub-foveal (SFCT), nasal, and temporal CT were 329.4±56.7μm, 302.3±63.5 μm, and 312.8± 56.7μm, respectively. CT varied significantly by location (p <0.001). CT was negatively correlated with age (r = -0.177, P <0.001). CT in emmetropic and myopic eyes was 319.7±53 μm and 313.1±53 μm, respectively. The difference in CT based on refractive status (p = 0.49) or sex was non-significant (p = 0.6). Regression analysis suggested that age (p <0.001), refractive error (p = 0.02), scanning time (p <0.001), and scanning location (p = 0.006) were significant predictors of CT. Conclusion CT measurements of the eyes of healthy Saudis can be used as reference values for studies evaluating CT changes due to various chorioretinal diseases.
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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 | 0.001 | 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) | 0.002 | 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".