Characterization of Ocular Surface Symptoms From Optometric Practices in North America
Bibliographic record
Abstract
PURPOSE: This study characterized ocular symptoms typical of dry eye in an unselected optometric clinical population in the United States and Canada. METHODS: Self-administered dry eye questionnaires, one for non-contact lens wearers (dry eye questionnaire) and one for contact lens wearers (contact lens dry eye questionnaire), were completed at six clinical sites in North America. Both questionnaires included categoric scales to measure the prevalence, frequency, diurnal severity, and intrusiveness of nine ocular surface symptoms. The questionnaires also asked how much these ocular symptoms affected daily activities and contained questions about computer use, medications, and allergies. The examining doctors, who were masked to questionnaire responses, recorded a nondirected dry eye diagnosis for each patient, based on their own diagnostic criteria. RESULTS: The dry eye questionnaires were completed by 1,054 patients. The most common ocular symptom was discomfort, with 64% of non--contact lens wearers and 79% of contact lens wearers reporting the symptom at least infrequently. There was a diurnal increase in the intensity of many symptoms, with symptoms such as discomfort, dryness, and visual changes reported to be more intense in the evening. The 22% percent of non-contact lens wearers and 15% of contact lens wearers diagnosed with dry eye (most in the mild to moderate categories) reported symptoms at a greater frequency than those not diagnosed with dry eye. CONCLUSIONS: Our results show that symptoms of ocular irritation and visual disturbances were relatively common in this unselected clinical population. The intensity of many ocular symptoms increased late in the day, which suggested that environmental factors played a role in the etiology of the symptoms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".