Neuro-Ophthalmological Optic Nerve Cupping: An Overview
Bibliographic record
Abstract
Optic nerve cupping or enlargement of the cup-to-disc ratio is widely recognized as a feature of glaucoma, however it may also occur in non-glaucomatous optic neuropathies. The most well-recognized non-glaucomatous optic neuropathies that cause cupping include compressive optic neuropathies, arteritic anterior ischemic optic neuropathies, hereditary optic neuropathies, and optic neuritis. Cupping is thought to consist of two main components: prelaminar and laminar thinning. The former is a shallow form of cupping and related to loss of retinal ganglion cells, whereas the latter involves damage to the lamina cribrosa and peripapillary scleral connective tissue. Differentiating glaucomatous and non-glaucomatous optic nerve cupping remains challenging even for experienced observers. Classically, the optic nerve in non-glaucomatous causes has pallor of the neuroretinal rim, but the optic nerve should not be examined in isolation. The patient's medical history, history of presenting illness, visual function (visual acuity, color vision and visual field testing) and ocular examination also need to be considered. Ancillary testing such as optical coherence tomography of the retinal nerve fiber layer and ganglion cell layer-inner plexiform layer may also be helpful in localizing the disease. In this review, we review the non-glaucomatous causes of cupping and provide an approach to evaluating a patient that presents with an enlarged cup-to-disc ratio.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".