Correlation of Tear Osmolarity and Dry Eye Symptoms in Convention Attendees
Bibliographic record
Abstract
PURPOSE: To assess the correlation between tear osmolarity readings and symptoms of dry eye in a nonclinical convenience sample and to determine how well symptoms and osmolarity correlate with the self-assessment of dry eye. METHODS: Two hundred forty-nine attendees in the exhibit hall at an optometric educational meeting agreed to participate in a dry eye study. Contact lens wearers were excluded. Volunteers supplied demographic information and completed a 5-item Dry Eye Questionnaire (DEQ-5) and answered the question "Do you think you have dry eye" with a yes or no response. Osmolarity testing was done using the TearLab instrument on the right eye, then on the left eye. Pearson correlation analyses were performed to determine the relationship between variables. RESULTS: There was no correlation between DEQ-5 scores and average tear osmolarity (correlation coefficient, 0.02) and highest osmolarity (correlation coefficient, 0.03). The mean DEQ-5 score was significantly higher among subjects who self-reported dry eye (mean, 11.3; p < 0.0001) compared with those who did not (mean, 5.4; p < 0.0001). No differences were observed between the yes and no self-reported dry eye groups and average osmolarity (p = 0.23) and highest osmolarity (p = 0.14). CONCLUSIONS: In this nonclinical population, there was no significant correlation between tear osmolarity and ocular symptoms as reported or between tear osmolarity and the self-assessment of dry eye.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".