How we treat patients with metastatic uveal melanoma
Bibliographic record
Abstract
Uveal melanoma is the most prevalent and aggressive intraocular malignancy affecting adults. Compared with cutaneous melanoma, uveal melanoma has distinct pathogenesis and molecular characteristics. Not surprisingly, it derives limited benefits from checkpoint inhibitors. Until recently, no systemic therapy had impacted survival outcomes for this patient population. Tebentafusp, a T-cell receptor-based molecule, is the first US Food and Drug Administration/European Medicines Agency-approved systemic therapy to improve the survival outcomes for uveal melanoma patients expressing HLA-A∗02:01. Only 45%-50% of this patient population will express the HLA-A∗02:01, however, and therefore are eligible to receive this novel treatment. Moreover, global access to tebentafusp is limited, and there are no guidelines to aid clinicians in decision-making regarding treatment. In this review, we outline our experience as Canada's largest tertiary referral centre in managing metastatic uveal melanoma patients and provide a comprehensive overview of the currently available treatment options, challenging scenarios, and ongoing clinical trials for patients with metastatic uveal melanoma.
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 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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".