Recent advances and future directions on the use of optical coherence tomography in neuro-ophthalmology
Bibliographic record
Abstract
Optical coherence tomography (OCT) is a noninvasive imaging technique used to qualitatively and quantitatively analyze various layers of the retina. OCT of the retinal nerve fiber layer (RNFL) and ganglion cell-inner plexiform layer (GCIPL) is particularly useful in neuro-ophthalmology for the evaluation of patients with optic neuropathies and retrochiasmal visual pathway disorders. OCT allows for an objective quantification of edema and atrophy of the RNFL and GCIPL, which may be evident before obvious clinical signs and visual dysfunction develop. Enhanced depth imaging OCT allows for visualization of deep structures of the optic nerve and has emerged as the gold standard for the detection of optic disc drusen. In the evaluation of compressive optic neuropathies, OCT RNFL and GCIPL thicknesses have been established as the most important visual prognostic factor. There is increasing evidence that inclusion of OCT as part of the diagnostic criteria for multiple sclerosis (MS) increases its sensitivity. Moreover, OCT of the RNFL and GCIPL may be helpful in the early detection and monitoring the treatment of conditions such as MS and Alzheimer's disease. OCT is an important aspect of the neuro-ophthalmologic assessment and its use is likely to increase moving forward.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".