Visibility of Optic Nerve Head Structures With Spectral-domain and Swept-source Optical Coherence Tomography
Bibliographic record
Abstract
PURPOSE: To compare the visibility of deep optic nerve head (ONH) structures and the visible area of the anterior surface of the lamina cribrosa (ASLC) with spectral-domain optical coherence tomography (SD-OCT) and swept-source OCT (SS-OCT). MATERIALS AND METHODS: In total, 33 glaucoma patients were imaged with SD-OCT (Spectralis, 24 radial B-scans centered on the ONH) and SS-OCT (Atlantis, 12 radial and a horizontal and vertical raster scan pattern containing 5 lines each, centered on the ONH). One of the SS-OCT horizontal and vertical scans that was best matching with the horizontal and vertical scan lines with those of SD-OCT was selected. All B-scans were then exported and deidentified. An independent observer determined whether the posterior choroid, border tissue, anterior scleral canal opening, and LC insertion into the sclera were detectable in the matched scan lines. Bruch membrane opening (BMO) and ASLC were segmented manually in radial scans. The segmented points were combined into a single plane and a linear interpolation was used to define BMO and ASLC areas. RESULTS: The posterior choroid, border tissue, and anterior scleral canal opening were detectable in most patients (94% to 100%, 88% to 100%, and 76% to 100%, respectively) and were not different between SD-OCT and SS-OCT. The LC insertion detection rate was nonstatistically higher for SS-OCT compared with SD-OCT (58% to 85% vs. 42% to 73%; P>0.10). The mean (SD) ASLC visible area (percentage of the respective BMO area) was 124 (30%) with SD-OCT and 135 (32%) with SS-OCT (P<0.01). CONCLUSION: SD-OCT and SS-OCT had comparable detection rates of deep ONH structures; however, a larger area of ASLC was visible with SS-OCT.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".