Ranibizumab versus bevacizumab for the treatment of neovascular age-related macular degeneration
Bibliographic record
Abstract
PURPOSE OF REVIEW: This paper reviews the recent literature regarding the effectiveness, efficacy and safety of intravitreal bevacizumab as compared with ranibizumab for the treatment of neovascular age-related macular degeneration (nAMD). RECENT FINDINGS: Numerous randomized clinical trials have demonstrated the safety and efficacy of ranibizumab for the treatment of nAMD. Bevacizumab, developed, labeled and approved for the management of colorectal cancer, has been used off-label for the management of nAMD. However, given its lower cost and effectiveness, it is commonly used for many cases of nAMD. Recent clinical trials have demonstrated similar effectiveness between the two compounds in terms of visual acuity and central macular thickness. However, emerging data have suggested that these two compounds may have different ocular and systemic adverse event profiles; bevacizumab has been linked to both a higher risk of severe intraocular inflammation and a higher risk of incident arterial thromboembolic events. This incremental risk for both ocular and systemic adverse events may have an impact on the incremental cost-effectiveness ratio derived from health economic models that directly compare one anti-vascular endothelial growth factor (VEGF) compound to the other. SUMMARY: Numerous clinical trials, including the Comparison of AMD Treatment Trial, are underway examining the comparative efficacy of ranibizumab versus bevacizumab for the treatment of nAMD. While these studies may demonstrate clinical noninferiority of one anti-VEGF compound over another, they may not be adequately powered to detect important differences in ocular and systemic safety. Large-scale, appropriately powered safety studies need to be conducted to evaluate differences in safety.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".