Prognostic and Predictive Value of ct<scp>DNA</scp> for Metastatic Uveal Melanoma: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
ABSTRACT Metastatic uveal melanoma (mUM) is a rare disease associated with poor prognosis and limited therapeutic options. Recent studies showed that detecting ctDNA is feasible and can aid treatment decisions for patients with mUM. We systematically searched PubMed, EMBASE, and Cochrane databases for eligible studies published up to May 2025 that included patients with mUM and reported data on the association between ctDNA and survival outcomes (OS and PFS). Statistical analyses were performed using Review Manager 5.4 software. Of the initial 450 records, seven studies met eligibility, including 518 patients with mUM. At baseline, ctDNA positivity was associated with significantly worse PFS (HR 2.34; 95% CI 1.56–3.51; p < 0.01; I 2 = 0%) and OS (HR 3.32; 95% CI 2.09–5.29; p < 0.01; I 2 = 48%). In patients treated with tebentafusp, ctDNA clearance was associated with superior OS (HR 0.19; 95% CI 0.07–0.49; p < 0.01; I 2 = 46%) and any decrease in ctDNA was associated with better OS (HR 0.42; 95% CI 0.22–0.80; p < 0.01; I 2 = 0%). This meta‐analysis underscores ctDNA as a potential predictor of worse survival in patients with mUM, highlighting its potential to refine risk stratification and guide treatment strategies. Trial Registration: International Prospective Register of Systematic Reviews (PROSPERO): CRD42025638076
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.034 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".