Topical Immunomodulators Improve Clinical Signs of Vernal Keratoconjunctivitis and Atopic Keratoconjunctivitis: A Meta-Analysis
Bibliographic record
Abstract
Abstract Objective: Topical immunomodulators cyclosporine A (CsA) and tacrolimus have been added in recent years to the armament to control severe chronic allergic ocular diseases such as atopic keratoconjunctivitis (AKC) and vernal keratoconjunctivitis (VKC). This meta-analysis summarizes the randomized controlled trials (RCTs) that utilized topical immunomodulators, to examine their effectiveness at decreasing clinical signs as assessed by clinicians in severe allergic eye disease.Methods: A systematic search identified thirteen studies and a total of 445 patients for inclusion, making this the largest meta-analysis published on the subject.Results: Thirteen RCTs were included. Eleven studies used Cyclosporine A as the treatment, and two used Tacrolimus. In total, 445 participants were included, 76.6% were male. The mean age of the participants was 14 years. All studies reported clinical signs as evaluated by an examining clinician. Signs were usually assessed by anatomical region, with the most common regions being the conjunctiva and the cornea, and the most common signs assessed were hyperemia and papillae. Three studies accounted for over 50% of the meta-analysis's weight. Effect size (d) ranged from -2.37 to -0.03, negative values favoring immunomodulators. Fixed Effect Meta-Analysis returned an SMD of -0.81 (95% CI: [-0.98, -0.65]). However, there was significant heterogeneity (I2=61%, Qw=30.76) in the outcome measure (P=0.0021); therefore, a random-effect meta-analysis was also completed where the pooled SMD was -0.98 (95% CI: [-1.26, -0.69], τ2 = 0.16).Conclusions: This study affirms that immunomodulators effectively treat clinical signs, including blepharitis, conjunctival hyperemia, edema, papillae, and corneal damage in severe ocular allergic disease.
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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.055 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".