Tocilizumab for Non-Infectious Uveitis: A Systematic Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 it