Infrared Imaging of Meibomian Glands and Evaluation of the Lipid Layer in Sjogren's Syndrome Patients and Nondry Eye Controls
Why this work is in the frame
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Bibliographic record
Abstract
PURPOSE: The purpose of this study was to evaluate meibomian gland dropout and lipid layer thickness (LLT) in patients with and without Sjögren's syndrome dry eye (SS). METHODS: We recruited 11 participants with SS (males/females [M/F], 1:10; mean age = 56.0 ± 9.1 years) and 10 control subjects without dry eye (M/F, 3:7; mean age = 58.5 ± 4.7 years). All participants completed the Ocular Surface Disease Index (OSDI) questionnaire. The LLT was assessed using the Tearscope Plus based on the appearance of the lipid layer. Noninvasive tear break-up time (NITBUT) also was measured. The lower and upper lids were everted, and the meibomian glands were imaged using the infrared camera of the Keratograph 4. A meibomian gland dropout score due to gland loss was obtained. Statistical analysis was conducted using the Mann-Whitney U test and correlations were determined using Spearman rank correlations. RESULTS: Of the SS participants, 100% reported ocular and oral dryness symptoms in the AECC questionnaire. The SS group recorded a higher OSDI score (median = 48.00, interquartile range [IQR] 23.0-56.2 vs. 2.1, IQR 0.0-2.6; P < 0.001), reduced LLT (median [IQR] = 15.0 [15.0-15.0] vs. 60.0 [45.0-100.0] nm; P = 0.001), and lower NITBUT (median [IQR] = 3.7 [2.5-4.2] vs. 9.5 [6.4-17.6] sec; P < 0.001) compared to the controls. Digital meibomian gland dropout score (% dropout) was significantly higher for the SS group (16.0% [IQR 12.1-40.0%] vs. 6.7% [IQR 1.5-12.7%]; P = 0.01). Subjective meibomian gland dropout score (0-6 score) was significantly higher for the SS group (median [IQR] = 1.5 [1.0-4.0] vs. 1.0 [0.0-1.25]; P = 0.03). CONCLUSIONS: Patients with SS showed higher meibomian gland dropout scores and reduced LLT and NITBUT, which likely contribute to the severe dry eye symptoms reported by SS subjects.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it