Innate and Adaptive Cell Populations Driving Inflammation in Dry Eye Disease
Bibliographic record
Abstract
Dry eye disease (DED) is the most common ocular disease and affects millions of individuals worldwide. DED encompasses a heterogeneous group of diseases that can be generally divided into two forms including aqueous-deficient and evaporative DED. Evidence suggests that these conditions arise from either failure of lacrimal gland secretion or low tear film quality. In its secondary form, DED is often associated with autoimmune diseases such as Sjögren's syndrome and rheumatoid arthritis. Current treatment strategies for DED are limited to anti-inflammatory medications that target the immune system as the source of deleterious inflammation and tissue injury. However, there is a lack of understanding of the underlying pathogenesis of DED, and subsequently, there are very few effective treatment strategies. The gap in our knowledge of the etiology of primary DED is in part because the majority of research in DED focused on secondary autoimmune causes. This review focuses on what is currently understood about the contribution of innate and adaptive immune cell populations in the pathogenesis of DED and highlights the need to continue investigating the central role of immunity driving DED.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".