Intra‐ and interobserver reproducibility of Bruch's membrane opening minimum rim width measurements with spectral domain optical coherence tomography
Bibliographic record
Abstract
PURPOSE: To investigate the reproducibility of Bruch's membrane opening minimum rim width (BMO-MRW) and retinal nerve fibre layer thickness (RNFLT) measurements using spectral domain optical coherence tomography (SD-OCT). Additionally, to investigate the reproducibility of BMO area measurements and fovea to BMO centre (FoBMO) angle. METHODS: Participants were healthy subjects (n = 30) and patients with glaucoma (n = 26). One eye of each participant was scanned to obtain optic nerve head (24 radial B-scans) and peripapillary (one circular B-scan) images by three independent examiners. Additionally, one examiner imaged each participant three times on the same day. Intra- and interobserver reproducibilities were estimated by within-subject standard deviation (SW) and coefficient of variation (COV). Spearman's rank correlation coefficient was used to test the correlation between the magnitude of the parameter and its standard deviation. RESULTS: The global BMO-MRW COVs (%) in healthy/glaucoma subjects were 0.87/1.34 and 1.28/3.13 for intra- and interobserver analyses, respectively, and the corresponding global RNFLT figures were 1.50/2.10 and 2.04/2.87. Global mean BMO-MRW and RNFLT showed no correlation with their respective standard deviations. The reproducibilities of BMO area and FoBMO angle were excellent and similar between the groups. CONCLUSION: The reproducibilities of BMO-MRW, BMO area measurements and FoBMO angle were excellent in both healthy subjects and patients with glaucoma. Bruch's membrane opening minimum rim width (BMO-MRW) reproducibility is comparable to that of RNFLT measurements.
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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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".