Stevens–Johnson Syndrome and Toxic Epidermal Necrolysis: A Systematic Review of Ophthalmic Management and Treatment
Bibliographic record
Abstract
Background: Stevens–Johnson Syndrome (SJS) and Toxic Epidermal Necrolysis (TEN) are rare, life-threatening mucocutaneous disorders often associated with severe ophthalmic complications. Ocular involvement occurs in 50–68% of cases and can result in permanent vision loss. Despite this, optimal management strategies remain unclear, and treatment practices vary widely. Methods: A systematic review was conducted in accordance with PRISMA guidelines and prospectively registered on PROSPERO (CRD420251022655). Medline, Embase, and CENTRAL were searched from 1998 to 2024 for English-language studies reporting treatment outcomes for ocular SJS/TEN. Results: A total of 194 studies encompassing 6698 treated eyes were included. Best-corrected visual acuity (BCVA) improved in 52.2% of eyes, epithelial regeneration occurred in 16.8%, and symptom relief was reported in 26.3%. Common treatments included topical therapy (n = 1424), mucosal grafts (n = 1220), contact lenses (n = 1134), amniotic membrane transplantation (AMT) (n = 889), systemic medical therapy (n = 524), and punctal occlusion (n = 456). Emerging therapies included TNF-alpha inhibitors, anti-VEGF agents, photodynamic therapy, and 5-fluorouracil. Conclusions: Disease-stage-specific therapy is crucial in ocular SJS/TEN. Acute interventions such as AMT may prevent long-term complications, while chronic care targets structural and tear-film abnormalities. Further prospective studies are needed to standardize care and optimize visual outcomes.
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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".