Low Vision and Dry Eye: Does One Diagnosis Overshadow the Other?
Bibliographic record
Abstract
SIGNIFICANCE: The prevalence of dry eye disease and low vision increases with age; they share risk factors and can be the result of underlying common causes. They are generally studied separately; however, combining these perspectives is relevant for research on assistive technology given that sustained focus affects the tear film because of decreased blinking rates. PURPOSE: The objective of this study was to elucidate to which extent dry eye disease risk factors, signs, and symptoms are assessed in low vision patients who receive an eye examination as part of their vision rehabilitation services. METHODS: Using a retrospective chart review, dry eye disease risk factors, signs, or symptoms were extracted from 201 randomly selected files that contained an eye examination in the past 5 years from two vision rehabilitation centers. RESULTS: Demographic variables of charts from the two sites did not differ (mean visual acuity, 0.85 logMAR [standard deviation, 0.53; range, 0 to 2.3]; mean age, 71.2 years [standard deviation, 19 years; range, 24 to 101 years]). Fifty charts (25%) mentioned at least one dry eye disease symptom. Sixty-one charts (30.3%) reported systemic medications that can exacerbate dry eye disease, whereas 99 (49.2%) contained at least one systemic disease thought to contribute to dry eye disease symptoms; 145 (72.1%) mentioned at least one type of ocular surgery. Artificial tears were documented in 74 charts (36.8%). Few specific dry eye tests were performed, with the exception of corneal integrity assessment reported in 18 charts (8.95%). CONCLUSIONS: Low vision patients have multiple risk factors for dry eye disease; however, dry eye disease tests were not frequently performed in comprehensive low vision eye examinations in this sample. More efforts should be made to assess dry eye disease to enhance comfort and functional vision, especially with the increasing demands of digital devices as visual aids.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".