The relief of dry eye signs and symptoms using a combination of lubricants, lid hygiene and ocular nutraceuticals
Bibliographic record
Abstract
PURPOSE: Nutrition on dry eye signs and symptoms. METHODS: This prospective study enrolled 28 dry eye participants. Participants were instructed to use the Lubricant Eye Drops at least 2-4× a day, SteriLid 1-2× a day, and Nutrition 3 gel caps once a day. Participants were followed up at baseline, 1 month and 3 months. Outcome variables were the Ocular Surface Disease Index (OSDI), Symptom Assessment iN Dry Eye (SANDE) questionnaire, non-invasive tear break-up time (NIBUT), osmolarity, number of meibomian glands blocked (#MG blocked), meibum quality, eyelid margin features, Schirmer's test, tear film lipid layer thickness (LLT), meniscus height, corneal and conjunctival staining. RESULTS: Twenty participants (mean age=43, from 23 to 66, 17F, 3M) completed the study. Participants reported having used, on average, the Lubricant Eye Drop 2.4×/day, the SteriLid 1.1×/day, and the Nutrition 3 gel caps 1×/day. There was a significant change over time (p<0.05) for OSDI (-21.2 points), SANDE (-32.4 points), NIBUT (+0.43s), eyelid margin features (-1.1 grade), meibum quality (-1.0 grade), and #MG blocked (-4.0 glands). CONCLUSION: Lubricant Eye Drop, SteriLid, and Nutrition, patients experience significant relief in both dry eye symptoms and signs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".