No Light Perception Vision in Neuro-Ophthalmology Practice
Bibliographic record
Abstract
BACKGROUND: To determine differential diagnosis and visual outcomes of patients with no light perception (NLP) vision related to neuro-ophthalmic conditions. METHODS: Retrospective case series of patients seen at tertiary neuro-ophthalmology practices. Patients were included if they had NLP vision any time during their clinical course. Outcome measures were final diagnosis, treatment, and visual outcome. RESULTS: Seventy-two eyes of 65 patients were included. The average age was 57.6 (range 18-93) years, and 58% were women. The Most common diagnosis (21 patients) was compressive optic neuropathy (CON) with meningioma being the most common culprit (12). Other diagnoses included optic neuritis (ON) (11 patients), infiltrative optic neuropathies (8), posterior ischemic optic neuropathy (7), nonarteritic anterior ischemic optic neuropathy (4), arteritic anterior ischemic optic neuropathy (3), ophthalmic artery occlusion (3), nonorganic vision loss (3), radiation-induced optic neuropathy (2), cortical vision loss (1), retinitis pigmentosa with optic disc drusen (1), and infectious optic neuropathy (1). Ten patients recovered vision: 7 ON, 2 infiltrative optic neuropathy, and 1 CON. Corticosteroids accelerated vision recovery in 7 of the 11 patients with ON to mean 20/60 (0.48 logMAR) over 9.0 ± 8.6 follow-up months. Eleven patients deteriorated to NLP after presenting with at least LP; their diagnoses included CON (3), ophthalmic artery occlusion (2), infiltration (2), ON (1), posterior ischemic optic neuropathy (1), arteritic anterior ischemic optic neuropathy (1), and radiation-induced optic neuropathy (1). CONCLUSIONS: NLP vision may occur because of various diagnoses. Vision recovery was mainly seen in patients with ON. Serious systemic conditions may present or relapse with NLP vision, which clinicians should consider as an alarming sign in patients with known malignancies.
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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 | 0.001 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 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".