Tocilizumab for Non-Infectious Uveitis: A Systematic Review
Bibliographic record
Abstract
Haixing Cao,* Kaiyu Bian,* Cong Ma, Naiwen Zhang, Xiang Ma Department of Ophthalmology, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning Province, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xiang Ma, Department of Ophthalmology, the First Affiliated Hospital of Dalian Medical University, 222 Zhongshan Road, Dalian City, Liaoning Province, 116011, People’s Republic of China, Tel +8618098876399, Email xma9467@vip.sina.comAbstract: Non-infectious uveitis (NIU) comprises a heterogeneous group of diseases causing severe ocular inflammation that threatens vision. In addition to visual impairment, patients frequently endure chronic pain, functional disorders, and psychosocial stress, all of which substantially reduce quality of life. Treating NIU remains challenging because many patients respond inadequately to high-dose corticosteroids and various immunosuppressants. This systematic review evaluated the efficacy and safety of tocilizumab (TCZ) in NIU treatment by analyzing case reports and small-scale studies. A systematic search of PubMed, Web of Science, and Embase up to May 1, 2025, identified all published cases reporting baseline and follow-up visual acuity alongside intervention details. The Newcastle-Ottawa Scale (NOS) assessed methodological quality, while the Joanna Briggs Institute (JBI) tool evaluated risk of bias. The systematic review included 96 patients (36 males, 60 females) with an average age of 35 years (range 4– 72). Behçet’s disease (BD) represented the most common underlying condition (33 cases), and panuveitis was the primary anatomical subtype (35 cases). Prior to TCZ initiation, patients had received an average of 2.8 conventional immunosuppressants and 1.6 biologics, yet persistent disease activity remained. The median interval from diagnosis to TCZ treatment was 11.8 months (range 4– 24). Following TCZ administration, vision improved in 62.5% of patients, intraocular inflammation was controlled in 83.3%, and macular edema resolved in 90.9%. Overall, 83.3% (80/96) responded favorably to TCZ. These findings indicate that TCZ may serve as an effective alternative for managing refractory NIU when other treatments fail.Keywords: non-infectious uveitis, uveitis, tocilizumab, TCZ, interleukin-6, IL-6
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".