Dry Eye Symptoms Assessed by Four Questionnaires
Bibliographic record
Abstract
PURPOSE: To establish the relationships between commonly used questionnaires including Dry Eye Questionnaire, McMonnies Questionnaire, and Ocular Surface Disease Index, and to test the construct and face validity of the simple Subjective Evaluation of Symptom of Dryness. METHODS: Ninety-seven non-contact lens wearing subjects were enrolled in the study and classified into either a "dry" and "non-dry" group using a single score from an initially applied subjective evaluation of symptom of dryness. The four questionnaires were then completed in a random order. The unidimensionality and accuracy of the responses was assessed using Rasch and receiver (or relative) operating characteristics curve analysis and the characteristics of and association between symptoms were compared using non-parametric statistics. RESULTS: The responses from the Dry Eye Questionnaire, McMonnies Questionnaire, and Ocular Surface Disease Index met the Rasch analysis criterion of unidimensionality. Each test separated the symptomatic and asymptomatic groups well [all receiver (or relative) operating characteristics area-under-the-curve statistics at least 0.88] and there were significant associations between the results from each questionnaire (all Spearman rho at least 0.64). CONCLUSIONS: The results illustrate that different questionnaire-based instruments examining symptoms in controls and symptomatic subjects derive unidimensional data that are similar inasmuch as the overall scores are highly correlated. The data also point to the utility of a quick, three-question screening tool in dry eye research.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".