Visual Loss from Choroidal Melanoma Mimicking Neurological Syndromes
Bibliographic record
Abstract
Melanoma of the eye is rare, but can mimic a range of disorders. This report highlights 2 cases of choroidal melanoma with vision loss mimicking neurological diagnoses. The first patient is a 41-year-old white male with a known history of multiple sclerosis and a previous episode of optic neuritis in the right eye, who presented with a 6-month history of decreased vision in the same eye, and occasional photopsiae. He was treated with 2 courses of oral steroids for presumed recurrent optic neuritis. After a temporary improvement in his symptoms, his vision worsened, following which he had a head MRI, which revealed a solid intraocular mass. He was subsequently diagnosed with a choroidal melanoma for which he was treated successfully with ruthenium-106 plaque brachytherapy. The second patient is a 57-year-old female, who presented with a progressive cerebellar syndrome under investigation by the neurology service, as well as decreased vision in the right eye. Her visual acuity gradually deteriorated and her neurological assessment, which included a PET-CT, revealed uptake in the right eye. The diagnosis of a choroidal melanoma was made, and following conservative treatment with proton beam radiotherapy, she had an enucleation of the eye. Intraocular tumours can masquerade as many different entities. Unexplained unilateral visual loss, especially if it is atypical for a neurological syndrome, should prompt dilated fundoscopy and referral to an ophthalmologist.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".