Clinical relevance of protruded retinal layers in minimum rim width measurement of the optic nerve head
Bibliographic record
Abstract
BACKGROUND/AIMS: Optical coherence tomography (OCT) imaging of the optic nerve head minimum rim width (MRW) has recently been shown to sometimes contain components besides extended retinal nerve fibre layer (RNFL). This study was conducted to determine whether excluding these components, termed protruded retinal layers (PRLs), from MRW increases diagnostic accuracy for detecting glaucoma. METHODS: In this cross-sectional study, we included 123 patients with glaucoma and 123 normal age-similar controls with OCT imaging of the optic nerve head (24 radial scans) and RNFL (circle scan). When present, PRLs were manually segmented, and adjusted MRW measurements were computed. We compared diagnostic accuracy of adjusted versus unadjusted MRW measurement. We also determined whether adjusted MRW correlates better with RNFL thickness compared with unadjusted MRW. RESULTS: The median (IQR) visual field mean deviation of patients and controls was -4.4 (-10.3 to -2.1) dB and 0.0 (-0.6 to 0.8) dB, respectively. In the 5904 individual B-scans, PRLs were identified less frequently in patients (448, 7.6%) compared with controls (728, 12.3%; p<0.01) and were present most frequently in the temporal sector of both groups. Areas under the receiver operating characteristic curves and sensitivity values at 95% specificity indicated that PRL adjustment did not improve diagnostic accuracy of MRW, globally or temporally. Furthermore, adjusting MRW for PRL did not improve its correlation with RNFL thickness in either group. CONCLUSION: While layers besides the RNFL are sometimes included in OCT measurements of MRW, subtracting these layers does not impact clinical utility.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".