A Comparative Study Between an Oil-in-Water Emulsion and Nonlipid Eye Drops Used for Rewetting Contact Lenses
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study was to determine the clinical impact of using SYSTANE BALANCE Lubricant Eye Drops (Alcon, Fort Worth, TX), an oil-in-water emulsion, as a rewetting eye drop in symptomatic contact lens wearers. METHODS: Subjects who had previously experienced contact lens discomfort (CLD), with a mean lens wearing history of 18.6±12.8 years, were randomly assigned to use a Test (SYSTANE BALANCE Lubricant Eye Drops; n=76) or control (habitual nonlipid contact lens rewetting eye drop; n=30) drop over their contact lenses within 5 min of lens insertion and then subsequently at 2 hr intervals up to a maximum of 4 drops per eye daily for a 1-month period. Assessments of subjective comfort, comfortable wearing time, lid wiper epitheliopathy (LWE), and corneal staining were conducted at baseline and after 1 month, after 6 hr of lens wear. RESULTS: Comfort, wearing time, LWE, and corneal staining all showed statistically significant improvements in the test group using SYSTANE BALANCE Lubricant Eye Drops at the 1-month visit compared with baseline data (all P<0.01) and compared with the control group at the 1-month visit (P<0.01, P=0.01, P<0.01, and P=0.03, respectively). CONCLUSIONS: The use of SYSTANE BALANCE Lubricant Eye Drops as a rewetting drop in a group of wearers who experienced symptoms of CLD improved subjective comfort scores, increased comfortable wearing time, and reduced signs of LWE and corneal staining, when compared with the use of non-lipid-containing contact lens rewetting eye drops.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".