The Relative Burden of Dry Eye in Patients’ Lives: Comparisons to a U.S. Normative Sample
Bibliographic record
Abstract
PURPOSE: To assess the relative burden of dry eye in daily life by comparing Short Form-36 (SF-36) responses from individuals with and without dry eye against U.S. norms. METHODS: Assessment of 210 people, 130 with non-Sjogren's keratoconjunctivitis sicca (non-SS KCS), 32 with Sjogren's Syndrome (SS), and 48 control subjects. The study population data and published normative SF-36 data were compared. Dry eye severity was assessed by recruited severity (control, non-SS KCS, SS), patient self-report (none, very mild/mild, moderate, severe/extremely severe), and clinician-report (none, mild, moderate, severe). Age- and gender-matched norms were compared with all defined severity groups. RESULTS: Compared with the norms, control subjects scored higher on all SF-36 scales. Effect size (ES) ranged from 0.15 to 0.52. Non-SS KCS patients had lower Role-Physical (ES=-0.07), Bodily Pain (ES=-0.08), and Vitality (ES=-0.11) scores, indicating more dry eye impact on those areas versus the norm. All SF-36 scale scores except Mental Health (ES=0.12) were lower in the SS group than the adjusted norm (ES range: -0.16 to -0.99). Regardless of severity classification, mild patients consistently had lower Role-Physical and Bodily Pain scores than the norm, suggesting impact on daily roles (ES < 0.2). Patients with moderately severe disease also experienced less vitality and poorer general health. The group with severe disease scored lower than the norm across all domains (ES range: -0.14 to -0.91) except Role-Emotional (ES=0.13) and Mental Health (ES=0.23). CONCLUSIONS: These results indicate dry eye's negative impact on everyday life, particularly in daily activities. Further research using disease-specific measures to examine dry eye's impact is underway.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".