The Performance of the Contact Lens Dry Eye Questionnaire as a Screening Survey for Contact Lens-related Dry Eye
Bibliographic record
Abstract
PURPOSE: The contact lens dry eye questionnaire (CLDEQ) is a self-administered survey developed to examine the distribution of dry eye symptoms among contact lens wearers. In this report, we examine the CLDEQ as a screening survey for contact lens-related dry eye and compare it with McMonnies' questionnaire. METHODS: The CLDEQ and McMonnies' questionnaire were administered to 367 unselected contact lens wearers at six clinics across the United States and Canada. After completion of the surveys, doctors unaware of the survey results completed a separate form indicating contact lens-related dry eye diagnosis at the end of a nondirected clinical examination. The CLDEQ is composed of nine habitual symptom subscales and a self-diagnosis question, which were tested for their predictive value for a diagnosis of contact lens-related dry eye. McMonnies' instrument was scored with use of the algorithm suggested in the literature. Sensitivity, specificity, and receiver operator characteristic (ROC) curve analyses were performed for each instrument on the basis of logistic regression results. RESULTS: The area under the ROC curve for the CLDEQ was 0.74, indicating moderate contact lens-related dry eye discrimination, and the Hosmer-Lemeshow goodness-of-fit test indicated that the CLDEQ was well calibrated (p = 0.84). The area under the ROC curve for McMonnies' questionnaire was 0.56, indicating poorer discrimination, and the Hosmer-Lemeshow goodness-of-fit test indicated it was also poorly calibrated (p = 0.08) for contact lens wearers. CONCLUSIONS: These analyses suggest that the CLDEQ is capable of discriminating contact lens-related dry eye and is accurate in doing so, especially in comparison with McMonnies' questionnaire. The CLDEQ is an efficient screening survey and may be used in future clinical research and epidemiologic studies of contact lens-related 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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".