Alterations in the Intraocular Cytokine Milieu after Intravitreal Bevacizumab
Bibliographic record
Abstract
PURPOSE: Several complications after intravitreal bevacizumab (IVB) treatment have been described including tears of the retinal pigment epithelium and tractional retinal detachment. The etiology of these complications remains unclear. The purpose of this study was to characterize changes in the intraocular levels of inflammatory cytokines after IVB as a possible explanation for these complications. METHODS: Twenty-nine patients with proliferative diabetic retinopathy (PDR) undergoing pars plana vitrectomy (PPV) for vitreous hemorrhage (VH) with IVB pretreatment were prospectively enrolled. Aqueous humor samples were taken at the time of IVB pretreatment and approximately 1 week later at the time of PPV. Multiplex cytokine arrays were used to assay 20 different cytokines. Multivariate general linear regression was performed to determine differences in cytokine levels between the two study visits. Proportional hazards regression was performed to determine the relationship between cytokine levels at PPV and postoperative outcomes. RESULTS: After treatment with IVB, vascular endothelial growth factor (VEGF) concentrations in the aqueous humor decreased (P = 0.0003), whereas the concentrations of IL-8 and transforming growth factor (TGF)-beta(2) increased after IVB (P < 0.03). The level of IL-8 at the time of PPV was associated with the occurrence of recurrent VH after surgery (hazard ratio, 1.32; P = 0.02). CONCLUSIONS: Alterations in the intraocular inflammatory cytokine milieu occur after IVB injection, possibly as a compensatory mechanism in response to VEGF inhibition. The increased concentrations of inflammatory cytokines after IVB may be clinically significant and may be responsible for some of the complications after IVB.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".