Efficacy of a new nanoemulsion artificial tear in dry eye disease management: Study protocol for a prospective cohort study
Bibliographic record
Abstract
BACKGROUND: Dry eye disease (DED) is a complex ocular disorder with a significant prevalence worldwide, especially in the Asian population. This study aimed to investigate changes in dry eye symptoms and signs following regular use of a new nanoemulsion eye drop, Systane COMPLETE Multi-Dose Preservative-Free (MDPF), in patients with mild to moderate DED in the Asian population. METHODS AND DESIGN: This is a prospective cohort study (ClinicalTrials.gov identifier: NCT06188260) that aims to recruit approximately 40 patients from the Asian population suffering from mild to moderate DED. Mild to moderate DED is defined according to the Tear Film and Ocular Surface Society (TFOS) Dry Eye Workshop (DEWS) II diagnostic criteria, including an Ocular Surface Disease Index (OSDI) score between 13-32, and with at least one of the following positive signs: corneal staining, Non-Invasive Tear Breakup Time (NITBUT), or osmolarity. The proposed follow-up period is 3 months. Patients undergo three assessments: baseline before using the eye drops, and follow-up visits after 2 weeks and 3 months regular use of the eye drops (four times daily). The primary outcome is the change in the OSDI score at 2 weeks. DISCUSSION: The results examine the dry eye symptoms before and after using the new nanoemulsion eye drop, Systane COMPLETE MDPF, in a cohort of mild to moderate DED sufferers. The findings may provide new treatment options for dry eye sufferers with significant clinical implications.
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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 | 0.017 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".