Optical coherence tomography: implications for neurology
Bibliographic record
Abstract
PURPOSE OF REVIEW: This article explores the role of optical coherence tomography (OCT) in neurology practice, particularly in diagnosing and monitoring conditions such as papilledema, optic neuritis, and retinal artery occlusion. OCT has been increasingly utilized as a noninvasive and effective tool for detecting and monitoring neuroaxonal damage in the visual pathway, which is important for early intervention and improved patient outcomes across a variety of neurologic conditions. RECENT FINDINGS: OCT as an imaging modality continues to demonstrate its utility in quantifying optic nerve and retinal changes reflecting neuroaxonal injury, including, peripapillary retinal nerve fiber layer (pRNFL) thickness and macular ganglion cell layer thickness (or volume). This review focuses on recent evidence regarding the utilization of this modality in diagnosing, monitoring, and quantifying treatment responses in patients with papilledema, optic neuritis, and retinal artery occlusion. Advances in OCT technology, including deep learning algorithms, continue to enhance the diagnostic accuracy and predictive capabilities in the field of neuro-ophthalmology. SUMMARY: In recent years, OCT has become an essential tool in neuro-ophthalmic assessment, offering precise structural and anatomical assessments that support diagnosis, treatment planning, and monitoring of conditions affecting the visual pathway. Ongoing advances in OCT technology are expected to further enhance its clinical utility.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".