Interocular Asymmetry of Minimum Rim Width and Retinal Nerve Fiber Layer Thickness in Healthy Brazilian Individuals
Bibliographic record
Abstract
PURPOSE: To determine interocular differences in Bruch's membrane opening minimum rim width (BMO-MRW) and retinal nerve fiber layer thickness (RNFLT) in healthy Brazilian individuals. MATERIALS AND METHODS: Both eyes of 220 healthy individuals were included in this observational, cross-sectional study. All individuals had normal clinical examination and visual fields. Global and sectorial interocular BMO-MRW and RNFLT differences, acquired and regionalized relative to the fovea to BMO center (FoBMO) axis, were calculated. The effect of age, axial length, and BMO area asymmetry on the parameters' asymmetry was evaluated. RESULTS: The 95th limits for interocular BMO-MRW and RNFLT global differences were 49 and 9 μm, respectively. BMO-MRW asymmetry was negatively correlated (β=-33.87 μm/mm, R=0.06, P<0.001), whereas RNFLT asymmetry was positively correlated (β= 6.13 μm/mm, R=0.09, P<0.001) with BMO area asymmetry. Neither BMO-MRW nor RNFLT asymmetries were correlated with axial length asymmetry (β=-16.90 μm/mm, R=0.00, P=0.15; β=-1.18 μm/mm, R=0.00, P=0.52, respectively). Similarly, BMO-MRW and RNFLT asymmetries were not correlated with age (β=0.17 μm/y, R=0.01, P=0.22; β=0.0 μm/y, R=0.00, P=0.19, respectively). CONCLUSIONS: Our results suggest that global BMO-MRW and RNFLT interocular differences exceeding 49 and 9 μm, respectively, may indicate statistically abnormal asymmetry, which may suggest early structural damage. Asymmetry in BMO area should be accounted for when considering interocular asymmetry in BMO-MRW and RNFLT.
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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 | 0.002 |
| 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) | 0.002 | 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".