Use of intravitreal rituximab for treatment of vitreoretinal lymphoma
Bibliographic record
Abstract
AIM: Vitreoretinal lymphoma is a diffuse large B cell non-Hodgkin lymphoma. Targeting malignant cells with rituximab is being used increasingly as local chemotherapy, but information on this treatment is scant. We aimed to describe current therapeutic approaches, as well as responses to and complications of, intravitreal rituximab in patients with vitreoretinal lymphoma. METHODS: Clinical data were collected in a standardised manner retrospectively on patients with vitreoretinal lymphoma treated with intravitreal rituximab. RESULTS: 48 eyes (34 patients) with vitreoretinal lymphoma were treated with a median of 3.5 intravitreal injections of rituximab (1 mg/0.1 mL) for new diagnosis (68.8%), progressive disease (29.9%) and maintenance therapy (2.1%). Intravitreal rituximab±methotrexate was the sole treatment in 19 eyes (39.6%). 31 eyes (64.6%) eyes achieved complete remission, after a median of 3 injections; 7 of these eyes developed recurrent disease. 11 eyes (22.9%) achieved partial remission. Although rituximab may have contributed to complications reported in 12 eyes (25.0%), a 2-line loss of Snellen visual acuity occurred in only 2 of those eyes (4.2%). CONCLUSIONS: Approaches in rituximab-based intravitreal chemotherapy vary widely, but our findings suggest that this treatment may be safe and effective in inducing remission in a majority of eyes with vitreoretinal lymphoma.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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 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".