Dry Eye Disease and Tear Cytokine Levels—A Meta-Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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