Effects of Three Eye Glass Frame Designs on Relative Humidity and Temperature Measured Over the Cornea
Bibliographic record
Abstract
Abstract Objective: To evaluate the relative humidity and temperature over the cornea in three different eye glass frame designs to see if frame design can influence relative humidity and temperature measured over the eyeball surface. Setting: Ophthalmology practice in the Denver, Colorado area Design: Prospective study Materials and Methods: Relative humidity and temperature were measured in 59 subjects in a clinical setting, with three different eye glass frame designs from June 2016 to September 2016. Measurements were made with small sensors mounted inside the eye glass lenses, to see if eyeglass frame design could influence these measured values. Baseline measures were taken with glasses off and follow-up measures with glasses on. Results: Relative humidity increased most markedly with a tight wraparound frame design (38.9%), followed by a smaller increase with post cataract wraparound sunglass frames (2.8%), and a minimal decrease with normal frames (-1.3 %). The temperature increased over the eyeball in all frame designs, but changed most dramatically in the tight wraparound frame (11.2 degrees). Conclusion: Tight wraparound eyeglass frames significantly increase the relative humidity over the cornea, as measured in this study. This frame design may offer another method of treatment for dry eye disease that is lower in cost than present therapies. Key Words: Dry Eye Disease, Relative Humidity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".