The Determinants of Macular and Peripapillary Retinal Thickness Using Optical Coherence Tomography
Bibliographic record
Abstract
Retinal nerve fiber layer thickness is an important factor in early diagnosis of posterior pole dysfunctions, assessment of treatment effect, and disease progress. The aim of this study was to compare the macular and peripapillary retinal thickness between genders and among refractive error types in healthy subjects. In addition, effective determinants of the thickness were ascertained. This cross-sectional study was conducted on 58 subjects (116 eyes), which had been referred to the Toos eye clinic of Mashhad, northeast of Iran, for refractive error surgery from September 2012 to June 2013. We used Optical Coherence Tomography for retinal thickness measurements. The mean±SD spherical equivalence was estimated to be -2.06±0.36 dioptres (range: -11.50, 7.38), axial length 23.89±0.14 mm, average peripapillary thickness 89.91±0.94 μm, average macular thickness 274.68±1.84 μm, and overall macular volume 9.89±0.07 mm3.Multiple linear regression modeling was indicated that axial length and gender had significant effect on average macular thickness. Axial length also showed substantial effect on average peripapillary thickness. Retinal thickness measurement regardless of refractive error type could lead to bias in disease diagnosis. The results of the present study might be used to enhance the assessment precision of ocular 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 | 0.001 | 0.002 |
| 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) | 0.001 | 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".