Antiviral and Anti-Inflammatory Therapeutic Interventions for Treating Herpes Stromal Keratitis: A Systematic Review
Bibliographic record
Abstract
Purpose Herpes stromal keratitis (HSK) is an immune-mediated corneal inflammation that occurs after a herpes simplex virus infection. This paper aims to systematically identify and compare interventions for treating HSK and their patient outcomes.Methods This systematic review followed the PRISMA methodology. Online databases were searched to obtain all relevant papers. Two independent reviewers screened through 168 records. Seven papers were included and used for data extraction. A qualitative analysis was conducted.Results HSK patients receiving prednisolone phosphate and acyclovir showed a higher treatment success rate and significantly longer time to failure compared to patients receiving only acyclovir (P < .001). No difference in resolution time was found between oral and topical acyclovir. Between groups receiving dexamethasone and flurbiprofen, resolution occurred in 93% and 67% of patients, and BCVA (LogMAR) improved from 1.0 to 0.30 and 0.48, respectively. BCVA improved in both cyclosporine-A (P < .001) and its control (prednisolone) groups (P = .002). A tacrolimus treatment group showed greater improvement in BCVA compared to its control (prednisolone) group (P < .001).Conclusion Corticosteroids and antivirals managed HSK most effectively only when used concurrently. Oral acyclovir showed similar effectiveness to its ointment counterpart, a preferable alternative for easier administration. Corticosteroid use could induce greater therapeutic benefits when tapered in concentration and frequency and administrated for at least 10 weeks. Anti-inflammatory drugs including flurbiprofen, cyclosporine-A, and tacrolimus could be safe and effective for treating HSK. Future long-term follow-up and RCTs could provide insights on the therapeutic benefits of these potential alternatives.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".