Impact of Dry Eye on Prolonged Reading
Bibliographic record
Abstract
SIGNIFICANCE: Patients with dry eye frequently report difficulty with reading. However, the impact of dry eye on reading has not been studied in detail. This study shows the unfavorable effect of dry eye on reading speed and offers mechanisms that may be responsible. PURPOSE: The purpose of this study was to evaluate the impact of dry eye signs as well as symptoms on both short-duration out-loud and prolonged silent reading. METHODS: This study included 116 patients with clinically significant dry eye, 39 patients with dry eye symptoms only, and 31 controls, 50 years or older. After the Ocular Surface Disease Index (OSDI) questionnaire, objective testing of dry eye (tear film stability studies, Schirmer's test, and ocular surface staining) was performed. Total OSDI score and two subscores (vision related and discomfort related) were calculated. A short-duration out-loud reading test and a 30-minute sustained silent reading test were performed. Reading speed for each test was calculated as words per minute (wpm) and compared across the three groups. RESULTS: Patients with clinically significant dry eye read slower than controls measured with sustained silent reading test (240 vs. 272 wpm, P = .04), but not with short-duration out-loud reading test (146 vs. 153 wpm, P = .47). Patients with dry eye symptoms only did not have slower reading speed measured using either reading test as compared with controls. However, vision-related OSDI subscore independently was associated with slower reading speed (P = .02). Multivariable regression models demonstrated that each 1-point (between 0 and 6) increase in corneal staining score led to a 10-wpm decrease in sustained silent reading speed (P = .01). CONCLUSIONS: This study demonstrates a significant negative impact of dry eye (particularly presence of corneal staining) on prolonged reading. Prolonged reading task may serve as an objective clinically relevant test to measure the impact of dry eye on vision-related quality of life.
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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 | 0.003 |
| 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) | 0.003 | 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".