The relief of dry eye signs and symptoms using a combination of lubricants, lid hygiene and ocular nutraceuticals
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 |
| 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) | 0.000 | 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 it