Topical pharmacologic treatments for dry eye disease: A systematic review
Bibliographic record
Abstract
BACKGROUND: Topical pharmacologic treatments for dry eye disease (DED) address different aspects of tear film deficiency by decreasing ocular surface inflammation, stimulating mucin secretion, increasing tear production, or reducing excessive evaporation. This systematic review evaluated randomized controlled trials (RCTs) and prospective observational studies of topical ophthalmic medications for DED. METHODS: PubMed and Embase were searched from 1980 to February 2024. For studies meeting inclusion criteria, efficacy outcomes (signs and symptoms of DED) and adverse event data were extracted. RESULTS: A total of 107 publications covering topical prescription medications (anti-inflammatory agents cyclosporine and lifitegrast; mucin secretagogues diquafosol and rebamipide; tear evaporation inhibitor perfluorohexyloctane; tear production stimulator nasal spray varenicline), other commercially available products, and novel agents in development were identified. In RCTs, significant improvements relative to a control group were demonstrated more often for sign endpoints (e.g., corneal staining, Schirmer score) than for symptom endpoints (e.g., eye dryness, ocular discomfort). The evaluated treatments were well tolerated; instillation site reactions were the most commonly reported adverse events. Year-long safety extension studies demonstrated maintenance of efficacy, with no new safety signals identified. Studies differed in design, methodology, control group, and outcomes assessment, making it difficult to compare across products, and head-to-head studies were rare. Several new products are in late-stage development, which will likely lead to additional treatment options. CONCLUSIONS: Current topical pharmacologic eye products improved signs, and sometimes symptoms, of DED and were well tolerated. Treatment selection should use a shared decision-making approach that takes DED etiology and patient preferences into account.
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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 | 0.006 |
| Bibliometrics | 0.007 | 0.008 |
| 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) | 0.006 | 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".