Dry Eye Disease and Tear Cytokine Levels—A Meta-Analysis
Bibliographic record
Abstract
Background-It is recognized that inflammation is an underlying cause of dry eye disease (DED), with cytokine release involved. We systematically reviewed literature with meta-analyses to quantitatively summarize the levels of tear cytokines in DED. Methods-The PubMed, Embase, Web of Science, Ovid, Cochrane, and Scopus databases were reviewed until September 2019, and original articles investigating tear cytokines in DED patients were included. Differences of cytokines levels of DED patients and controls were summarized by standardized mean differences (SMD) using a random effects model. Study quality was assessed by applying Newcastle-Ottawa-Scale and the GRADE quality score. Methods of analytical procedures were included as covariate. Results-Thirteen articles investigating 342 DED patients and 205 healthy controls were included in the meta-analysis. The overall methodological quality of these studies was moderate. Systematic review of the selected articles revealed that DED patients had higher tear levels of interleukin (IL)-1β, IL-6, chemokine IL-8, IL-10, interferon-γ, IFN-γ, and tumor necrosis factor-α, TNF-α as compared to controls. Evidence was less strong for IL-2 and IL-17A. Conclusions-Data show that levels of tear cytokines in DED and control display a great variability, and further studies of higher quality enrolling a higher number of subjects are needed, to define a cut-off value.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.039 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".