Efficacy of rituximab in non-paraneoplastic autoimmune retinopathy
Bibliographic record
Abstract
BACKGROUND: Autoimmune retinopathy (AIR) is a rare but potentially blinding condition that is often underdiagnosed. Common features in AIR presentation include rapidly progressive vision loss with abnormal electrophysiological responses of the retina associated with positive anti-retinal antibodies. AIR is also challenging to treat, and thus, the introduction of new potential therapeutic agents is welcomed. The goal of this communication is to assess the effects of rituximab infusions on electroretinogram (ERG) responses and visual function outcomes in patients with non-paraneoplastic autoimmune retinopathy (npAIR). RESULTS: Following infusion(s), three out of five patients showed no evidence of disease progression or improved, while two patients continued to progress on ERG. One patient demonstrated improvement in visual acuity (2 lines) in both eyes. ERG responses provided objective monitoring of patients' visual function and response to immunosuppression over time. CONCLUSIONS: These findings suggest that patients with npAIR unresponsive to other immunosuppression therapies may benefit from rituximab infusion, although stabilization rather than improvement was more frequently the outcome in our case series. Furthermore, regularly scheduled ERG follow-up examinations are recommended for monitoring patients' progression during treatment.
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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) | 0.001 | 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 | 0.001 |
| 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".