Reduction of Artificial Tears and Use of Adjunctive Dry Eye Therapies After Lifitegrast Treatment: Evidence from Clinical and Real-World Studies
Bibliographic record
Abstract
Purpose: To assess the frequency of patients reducing the use of artificial tears (ATs) among patients with dry eye disease (DED) following lifitegrast treatment. Patients and Methods: Two independent analyses were performed using the data from the 1-year, randomized, multicenter, Phase 3 SONATA trial and a noninterventional, real-world evidence (RWE) study conducted in patients with DED who were treated with lifitegrast in the United States and Canada. In SONATA, patients who had used ATs in the lifitegrast and placebo groups were included. The RWE study reviewed patients' electronic medical records, prescribing patterns, and practices of physicians throughout the survey. These data were then used to compare the proportion of patients using ATs in the 6-month pre-index period versus the 12-month post-index period. Results: Of 293 patients (lifitegrast, n=195; placebo, n=98) from SONATA, 107 (lifitegrast, n=64; placebo, n=43) used ATs during the on-therapy period while 186 (lifitegrast, n=131; placebo, n=55) did not. Of those not using ATs, the proportion of patients in the lifitegrast group at any time was higher (~67% [n=131]) versus placebo (~56% [n=55]); this was the case at all study time-points (Days 90, 180, 270, and 360). The RWE study included 600 patient charts (US, n=550; Canada, n=50); 75.5% (n=453) reported AT use. There was ~40% decrease in the proportion of patients using ATs as adjunct DED therapy to lifitegrast in the post-index period (n=273) versus those in the pre-index period (n=453). Conclusion: The findings show that the reliance on AT use can be gradually reduced with lifitegrast treatment, eventually leading to a reduction in disease burden.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".