Changes in aqueous and vitreous inflammatory cytokine levels in neovascular age‐related macular degeneration: a systematic review and meta‐analysis
Bibliographic record
Abstract
Inflammatory cytokines are involved in the pathogenesis of neovascular age-related macular degeneration (nAMD) and have been shown to be useful as diagnostic and predictive biomarkers. Given the heterogeneity of data within the literature, we aimed to quantitatively summarize data related to inflammatory cytokines in nAMD. A systematic search without year limitation was performed up to 13 April 2020. Studies were included if they provided data on aqueous or vitreous cytokine concentrations in patients with nAMD. Data were extracted from 95 studies that encompassed 3105 study eyes with nAMD and 1209 control eyes. Effect sizes were generated as standardized mean differences (SMDs) of cytokine concentrations between patients with nAMD and controls. Among the 4314 eyes in 95 studies, aqueous concentrations (standard mean difference, 95% confidence interval and p-value) of MCP-1 (0.43, 0.09 to 0.77 and p = 0.01), MIG (0.63, 0.31 to 0.94 and p = 0.0001), TGF-β (0.45, 0.07 to 0.82 and p = 0.02) and VEGF (0.64, 0.31 to 0.98 and p = 0.0001) were significantly higher in patients with nAMD compared to healthy controls. No differences, failed sensitivity analyses or insufficient data were found between patients with nAMD and healthy controls for the concentrations of the remaining cytokines and with all vitreous samples. Previous studies had shown conflicting associations with nAMD for all 27 cytokines assessed. Our analysis indicates multiple candidate cytokines other than VEGF that are implicated in nAMD and adds clarity to the previous literature. This will help focus translational research in nAMD investigating biomarkers and therapeutic targets.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.025 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".