Evaluation of Dry Eye Disease in Children With Systemic Lupus Erythematosus and Healthy Controls
Bibliographic record
Abstract
PURPOSE: To compare the symptoms and signs of dry eye disease (DED) in children with systemic lupus erythematosus (SLE) with those in healthy children using common diagnostic tools. METHODS: Prospective, observational, single-center cohort study. Thirty-four subjects with SLE and 15 healthy subjects were recruited from the Hospital for Sick Children in Toronto, Canada. Subjects underwent subjective and objective dry eye assessments using the Canadian Dry Eye Assessment (CDEA) questionnaire, tear film osmolarity, slit lamp examination, tear film break-up time, corneal fluorescein staining, Schirmer test 1, and conjunctival lissamine green staining. RESULTS: No difference in symptoms was found between children with SLE and healthy children (CDEA score 6.4 ± 5.4 vs. 3.8 ± 3.2; P = 0.09). Corneal staining was more prevalent in children with SLE than in healthy children (58.8% vs. 20.0%; P = 0.01), and children with SLE had higher mean corneal fluorescein staining scores (1.7 ± 1.7 vs. 0.2 ± 0.4; P = 0.002). No statistically significant differences in tear osmolarity, inter-eye differences in tear osmolarity, tear film break-up time, Schirmer test 1, or lissamine green staining scores were observed between the 2 groups. In healthy children, CDEA scores weakly correlated with corneal fluorescein staining score (r = 0.53, P = 0.04). In children with SLE, no correlation between CDEA score and any of the diagnostic test outcomes was found. CONCLUSIONS: There is discordance between symptoms and signs of DED in children with SLE. Corneal fluorescein staining is essential for the diagnosis of DED in these children.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".