Neovascular age-related macular degeneration: intraocular inflammatory cytokines in the poor responder to ranibizumab treatment
Bibliographic record
Abstract
Purpose: To determine the levels of interleukin (IL)-6, vascular endothelial growth factor-A, platelet-derived growth factor, placental growth factor (PLGF), and other cytokines in the aqueous fluid of patients with neovascular age-related macular degeneration who respond poorly to ranibizumab. Patients and methods: This is an observational, prospective study. Thirty-two eyes from 30 patients were included in the study: 11 patients who responded poorly to ranibizumab and were switched to aflibercept (AF group), 8 patients who received ranibizumab and photodynamic therapy (PDT group), and 13 patients who responded to ranibizumab (control group). Aqueous fluid samples were collected for analysis of cytokine levels at baseline and after 1, 2, and 3 months of treatment. The effect of treatment on cytokine levels was compared between the study groups and between different time points using a linear mixed-effect regression model. Results: In the AF group, there was an increase in vascular endothelial growth factor-C, IL-7, and angiopoeitin-2 levels ( P =0.01) and a decrease in intercellular adhesion molecule and IL-17 levels ( P =0.01) between baseline and 3 months. After adjustment for age, sex, race, and type of lesion at baseline, the PLGF level was higher ( P =0.02) and the IL-7 level was lower ( P =0.04) in the ranibizumab non-responder group than in the ranibizumab responder group. Conclusion: Switching from ranibizumab to aflibercept did not reduce intraocular levels of angiogenesis cytokines, but resulted in improvement of central subfield thickness. PLGF levels were higher in poor responders to ranibizumab. The response of lesions to medication might be related to the stage of choroidal neovascularization. Trial registration: www.ClinicalTrial.gov (NCT02218177c). Keywords: neovascular age-related macular degeneration, choroidal neovascularization, anti-VEGF non-responder, poor responder, ranibizumab and AMD
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".