Optic neuropathy in the context of leukemia or lymphoma: diagnostic approach to a neuro-oncologic emergency
Bibliographic record
Abstract
BACKGROUND: Optic neuropathy in the context of leukemia or lymphoma has a broad differential diagnosis, including infiltration, infection, inflammation, compression, and medication effects. Confirming the underlying etiology in a timely manner is crucial as, while infiltration carries a poor prognosis, treatment modalities can have serious consequences themselves. METHODS: A review of the literature was conducted for cases of isolated optic neuropathy in the context of leukemia or lymphoma, in which the underlying etiology remained unclear following initial clinical examination and neuroimaging. Clinical, radiological, and pathological characteristics of the cases are summarized. RESULTS: Ninety-two cases meeting inclusion criteria were identified. Leukemic or lymphomatous infiltration was the presumed diagnosis in 72% of the reports, indicating this is the most likely etiology in such cases. The remaining reports were attributed to inflammation, infection, or drug toxicity. For illustrative purposes, the previously unpublished case of an 11-year-old girl with remitted T lymphoblastic lymphoma is presented. She suffered recurrence in the form of isolated left optic nerve infiltration that required transconjunctival biopsy to confirm diagnosis. CONCLUSIONS: Optic nerve infiltration by leukemia or lymphoma requires both diagnostic certainty and urgent management. Recommendations are made for a step-wise, yet rapid investigative approach that may ultimately require biopsy of the optic nerve.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".