Determinants and Characteristics of Bruch's Membrane Opening and Bruch's Membrane Opening–Minimum Rim Width in a Normal Japanese Population
Bibliographic record
Abstract
Purpose: To identify determinants of Bruch's membrane opening (BMO), and BMO-minimum rim width (BMO-MRW) and circumpapillary retinal nerve fiber layer thickness (RNFLT) centered on BMO center and characterize these parameters in a normal Japanese population. Methods: Spectral-domain optical coherence tomography images of optic nerve head and circumpapillary and macular retina were obtained in 258 eyes of 258 normal Japanese with mean (standard deviation) age of 51.7 (18.2) years. BMO area, BMO-MRW, RNFLT (measured with a 3.5-mm-diameter circle scan) were all acquired and analyzed relative to the eye-specific fovea to BMO (FoBMO) axis. One randomly selected eye of each subject was analyzed. Multiple regression analysis was used to identify determinants to the parameters. Results: BMO area, global BMO-MRW, RNFLT, and FoBMO angle averaged 2.06 (0.45) mm2, 305.5 (50.0) μm, 101.8 (9.6) μm, and -7.8° (3.8°), respectively. There was a modest correlation between global BMO-MRW and RNFLT (r = 0.337; P < 0.001), while the sectorwise correlations were highest in the superior-temporal sector (r = 0.500; P < 0.001) and lowest in the nasal sector (r = 0.117; P = 0.063). Global BMO-MRW and RNFLT declined with age at -1.04 μm/y (P < 0.001) and -0.12 μm/y (P = 0.001), and the former correlated negatively (P = 0.001) and the latter positively (P < 0.001) with BMO area after adjustment for other factors (R2 = 0.191 and 0.272, respectively). BMO area correlated positively with axial length (P = 0.023) and negatively with age (P < 0.001) (R2 = 0.157). Conclusions: BMO-MRW and RNFLT declined with age with a difference between them in their relationship to BMO area. BMO area positively correlated with axial length and negatively with age.
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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 | 0.001 | 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".