Efficacy of topical ophthalmic drugs in the treatment of dry eye disease: A systematic literature review
Bibliographic record
Abstract
Dry eye disease (DED) is a multifactorial and complex disease of the ocular surface, with a high prevalence in adults. We systematically reviewed efficacy and safety data from published articles reporting results from prospective, controlled trials of topical ophthalmic drugs for DED. PubMed was searched for articles from January 1997 to October 2017. Twenty-six unique trials investigating 13 ophthalmic drugs were identified, including trials of the approved drugs cyclosporine A, cyclosporine A cationic emulsion, diquafosol, rebamipide and lifitegrast. All identified studies provided level 1 evidence. None of the large (N > 100) studies demonstrated statistical significance of primary endpoints for both a sign and a symptom endpoint versus a control treatment in the same published trial. Publications on lifitegrast reported statistical superiority in a symptom or sign endpoint versus the control group in a large (N > 200), multicenter trial, with results repeated in trials of similar design. The most common adverse events associated with the approved drugs related to ocular discomfort upon instillation, especially burning/stinging and ocular irritation. The trial design and endpoints used across the studies varied considerably, highlighting the importance of standardization in clinical trials for DED. Recent advances in drug delivery and improved understanding of DED should contribute to new ophthalmic drug approvals.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".