<p>Efficacy and Safety of Phospholipid Nanoemulsion-Based Ocular Lubricant for the Management of Various Subtypes of Dry Eye Disease: A Phase IV, Multicenter Trial</p>
Bibliographic record
Abstract
PURPOSE: Complete) lubricant eye drops in participants with dry eye disease (DED). PARTICIPANTS AND METHODS: In this phase IV, open-label, single-arm, interventional, multicenter study, adult participants with DED - subtyped into aqueous deficient, evaporative, and mixed dry eye - were instructed to instill one drop of PG-HPG in each eye twice a day for 28 days. Endpoints included change from baseline in tear film break-up time (TFBUT) (primary) and ocular discomfort visual analog scale (VAS) score at Day 14 and TFBUT at Day 28 (secondary). Safety was assessed throughout the study. Data were analyzed for overall patient cohort and by DED subtypes. RESULTS: A total of 134 participants received treatment (mean age: 56.6 years; female: 75.4%). At Day 14, the mean (standard deviation [SD]) TFBUT increased from 2.6 (1.00) seconds at baseline to 4.2 (2.92) seconds (mean change: 1.5 [2.80] seconds); median change was 0.8 seconds (95% CI: 0.52, 1.19) for the overall cohort; the change was maintained at Day 28 (mean change: 1.4 (2.80) seconds, median change: 0.8 seconds (95% CI: 0.49, 1.17). Subgroup analysis showed a mean change (SD) from baseline in TFBUT of 1.1 (2.41) seconds for aqueous deficient, 2.4 (3.17) seconds for evaporative, and 1.2 (2.63) seconds for mixed dry eye at Day 14, respectively. The ocular discomfort VAS score improved (decreased) from baseline at Day 14 (mean [SD] change: -17.3 [24.80]) for the overall cohort, -22.0 [21.73] for aqueous deficient, -17.6 [24.17] for evaporative and -13.1 [27.49] for mixed dry eye subgroups). Adverse events (AEs) were reported in 9 (6.7%) participants. No serious AEs were reported during the study. CONCLUSION: Our study findings demonstrated that PG-HPG-based nanoemulsion lubricant eye drops were effective and well tolerated in participants with DED and all of its subtypes.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".