Psychometric Properties and Validation of the Standard Patient Evaluation of Eye Dryness Questionnaire
Bibliographic record
Abstract
PURPOSE: To characterize the psychometric properties of the standard patient evaluation of eye dryness (SPEED) questionnaire and to validate and compare its performance with 4 existing dry eye questionnaires. METHODS: A total of 50 subjects (40 female and 10 male) were enrolled; of these, 30 were symptomatic and 20 asymptomatic, as determined using the ocular surface disease index (OSDI). This study consisted of 2 visits in which all subjects completed 5 different dry eye questionnaires (SPEED, OSDI, dry eye questionnaire, McMonnies dry eye questionnaire, and subjective evaluation of symptom of dryness) in random order at each visit. Clinical measurements were obtained on the first visit. Repeatability was determined using concordance correlation coefficient; dimensionality was determined using principal component, factor, and Rasch analyses; and validity was determined by comparing SPEED scores with dry eye diagnosis based on OSDI (primarily using receiver-operator curve analysis). RESULTS: The SPEED questionnaire data were found to be unidimensional and repeatable. Three principal components (dryness, burning, and soreness/fatigue) were identified and SPEED between visit concordance correlation coefficient was 0.923 (95% confidence interval, 0.868-0.955). The area under the receiver-operator curves was 0.928. The only clinical measures that correlated "well" with SPEED questionnaire scores were corneal staining (P < 0.05), meibomian gland score (P < 0.05), and meibomian glands yielding liquid secretion score (P < 0.05). CONCLUSIONS: The SPEED questionnaire was shown to be a repeatable and valid instrument for measurement of dry eye symptoms. The SPEED score also correlated significantly with ocular surface staining and clinical measures of meibomian gland function.
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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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".