Infrared Imaging of Meibomian Glands and Evaluation of the Lipid Layer in Sjogren's Syndrome Patients and Nondry Eye Controls
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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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".