Metastatic Uveal Melanoma Surveillance: A Delphi Panel Consensus
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Uveal melanoma is a rare but aggressive intraocular malignancy that metastasizes in up to half of patients, most commonly to the liver, despite effective local treatment. In the absence of robust evidence, there are no standardized guidelines for post-treatment surveillance, resulting in wide variation in imaging modalities, frequency, and duration across physicians and institutions. This study aimed to develop expert consensus recommendations for surveillance strategies in patients with uveal melanoma. METHODS: A modified Delphi method was conducted across three iterative survey rounds between September 2024 and February 2025 using an online platform. Panelists included medical oncologists, ocular oncologists, radiologists, and surgical oncologists from North America. A multidisciplinary steering committee developed statements addressing risk-based surveillance using both molecular and clinical prognostic factors, including gene expression profiling (GEP) and PRAME status. Consensus was defined a priori as ≥70% of panelists rating a statement 7-9 on a 9-point Likert scale. RESULTS: Forty-nine experts were invited, and 41 completed at least one survey round. The panel represented 17 U.S. states, Washington, D.C., and two Canadian provinces. Twelve statements reached stable consensus, including recommendations for imaging modality, frequency, and duration in intermediate- and high-risk patients. Although there was agreement that low-risk patients warrant surveillance, no consensus was reached on the optimal approach for this group. CONCLUSIONS: This is the first study to provide consensus-based guidance incorporating GEP and PRAME status into surveillance recommendations for uveal melanoma, offering a standardized framework to guide clinical practice and future research.
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 | 0.220 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".