An Eyelid Warming Device for the Management of Meibomian Gland Dysfunction
Bibliographic record
Abstract
PURPOSE: To determine the effectiveness of the MGDRx EyeBag in managing meibomian gland dysfunction. METHODS: This was a prospective, randomized, controlled, observer-masked, bilateral eye study that enrolled 29 participants. Participants were randomized into either the EyeBag group or the control group. The EyeBag group used the EyeBag 10minutes 2x/day, and the control group remained on their own dry eye treatment regimen (if applicable). All participants were observed at baseline, 2 weeks (2wk) and 4 weeks (4wk). At 4wk, participants in the EyeBag group were asked to stop using the EyeBag. All participants were seen again at 8 weeks (8wk). Primary outcomes were the Ocular Surface Disease Index (OSDI), Current Symptoms Questionnaire (CSQ), meibomian gland score (MG score), and non-invasive tear breakup time (NIBUT). RESULTS: Twenty-five participants completed the study (mean age 38±15 years, 7 male). There was a significant change in OSDI over time for the EyeBag group (mean[lower 95% CI, upper 95% CI], baseline: 39.1[31.1,47.0], 2wk: 26.8[19.7,33.9], 4wk: 26.6[16.5,36.7], 8wk: 27.7[18.4,37.0]; p=0.01), but not in the control group (p=0.22), but no significant difference between groups at all time points (all p>0.27). Symptoms immediately improved after conducting the EyeBag based on at-home CSQ scores (Δ=-5.0 points, p<0.01), but not in the control group. For both groups, there was no significant change (p-value EyeBag,p-value control) in MG score (0.21,0.17) and NIBUT (0.49,0.06) over time. CONCLUSIONS: The EyeBag may relieve symptoms of dry eye, but the effect on meibomian gland function and tear stability when used for only 4 weeks was undetectable.
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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 | 0.001 | 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 | 0.001 |
| 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".