Bruch's membrane opening minimum rim width and retinal nerve fiber layer thickness in a Brazilian population of healthy subjects
Bibliographic record
Abstract
OBJECTIVE: To determine Bruch's membrane opening (BMO) minimum rim width (MRW) and peripapillary retinal nerve fiber layer thickness (RNFLT) measurements, acquired with optical coherence tomography (OCT) in healthy Brazilian individuals self-reported as African Descent (AD), European Descent (ED) and Mixed Descent (MD). METHODS: 260 healthy individuals (78 AD, 103 ED and 79 MD) were included in this cross-sectional study conducted at the Clinics Hospital of the University of Campinas. We obtained optic nerve head (24 radial B scans) and peripapillary retinal nerve fiber layer (3.5-mm circle scan) images in one randomly selected eye of each subject. RESULTS: After adjustment for BMO area and age, there were no significant differences in mean global MRW (P = 0.63) or RNFLT (P = 0.07) among the three groups. Regionally, there were no significant differences in either MRW or RNFLT in most sectors, except in the superonasal sector, in which both MRW and RNFLT were thinner among ED (P = 0.04, P<0.001, respectively). RNFLT was also thinner in ED in the inferonasal sector (P = 0.009). In all races, global MRW decreased and global RNFLT increased with BMO area. AD subjects had higher rates of global RNFLT decay with age (-0.32 μm/year) compared to ED and MD subjects (-0.10 μm/year and -0.08 μm/year, respectively; P = 0.01 and P = 0.02, respectively). CONCLUSIONS AND RELEVANCE: While we found no significant differences in global MRW and RNFLT among the three races, age-related thinning of the RNFLT was significantly higher in the AD subgroup, which warrants further study.
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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 | 0.001 |
| 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".