Rapid growth of primary uveal melanoma following intravitreal bevacizumab injection: a case report and review of the literature
Bibliographic record
Abstract
Uveal melanoma size is a significant predictor of tumor metastasis. Although the relationship between antivascular endothelial growth factors (VEGF) and uveal melanoma growth has been studied, results are paradoxical, and the relationship remains controversial. We report the case of a 65-year-old man who presented with elevated intraocular pressure in his right eye, neovascularization of his iris, and significant corneal edema, which obscured the view of the angle. Given his history of proliferative diabetic retinopathy, he was diagnosed with neovascular glaucoma and subsequently received an intravitreal injection of bevacizumab and underwent Ahmed valve insertion. This was complicated by postoperative hyphema. Two and a half months postoperatively, a mass involving the inferior iris and ciliary body became visible, and fine-needle aspiration biopsy confirmed uveal melanoma. Seven weeks after diagnosis, the tumor's largest basal diameter had increased from 2.51 mm to 18.0 mm, and apical height increased from 6.23 mm to 11.0 mm. His right eye was enucleated. Histopathological analysis showed discontinuous invasion next to the Ahmed valve. Tumor progression after injection raises the possibility that in some untreated uveal melanomas, accelerated growth may occur following exposure to anti-VEGF agents.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".