The Relationship between Habitual Patient-Reported Symptoms and Clinical Signs among Patients with Dry Eye of Varying Severity
Bibliographic record
Abstract
PURPOSE: To investigate symptom profiles and clinical signs in subjects with dry eye and normal subjects in a cross-sectional multicenter study. METHODS: Subjects aged 35 to 65 were recruited according to dry eye diagnostic codes and telephone interview and completed the Dry Eye Questionnaire 2001, among others, and underwent dry eye clinical tests. RESULTS: Subjects (122) included 28 control subjects (C), 73 with non-Sjögren's keratoconjunctivitis sicca (non-SS KCS) and 21 with Sjögren's syndrome (SS). Subjects with SS or non-SS KCS reported discomfort and dryness most frequently and that many symptoms worsened over the day and were quite bothersome. Groups were significantly different in corneal fluorescein staining, conjunctival lissamine green staining, Schirmer 1 tear test, and tear break-up time (TBUT; chi2 and Kruskal-Wallis, P<0.0001). Statistically significant, but moderate, correlations were found between the frequency and evening intensity of dryness and discomfort and TBUT, Schirmer's tear test, overall corneal fluorescein staining, and temporal lissamine green conjunctival staining (Spearman r=0.31-0.45, P<0.01). Symptoms were moderately to highly correlated with the clinician's global grading of severity and highly correlated to patient's self-assessment of severity (r=0.46-0.86, P<0.0001), whereas signs showed lower correlations (r=0.22-0.46, P<0.0001). CONCLUSIONS: Subjects with SS or non-SS KCS reported frequent and intense ocular surface symptoms in the evening, some of which correlated moderately with clinical test results. The global clinician grade of dry eye correlated more highly with patient symptoms than did clinical signs, suggesting that patient symptoms influence dry eye diagnosis and grading of dry eye more than clinical test results.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".