Systemic Surveillance Guidelines for Uveal Melanoma: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Uveal melanoma (UM) is the most common primary intraocular tumour. Despite effective local therapies, UM has a high risk of metastatic recurrence, most frequently to the liver. A significant proportion of patients treated definitively for primary UM eventually experience metastatic disease. Systemic surveillance to detect recurrence is critical to maximise therapeutic options. Whilst international guidelines exist, there are currently no standardised Australian guidelines for surveillance imaging. This systematic review examines the literature regarding systemic surveillance methods following local treatment for UM. METHODS: Medline, Embase and PubMed databases were searched, from 2010 to 01-07-2024, using keywords related to uveal melanoma and surveillance. Eligible studies were identified by two independent reviewers, and a systematic review was undertaken. RESULTS: Of 840 records, six guidelines and institutional consensus statements were identified, and an additional 13 studies were included. Most studies were cohort studies (n = 7), with the rest being case-control studies and reliability analyses. Risk stratification methods and surveillance strategies varied, with most studies recommending increased frequency (at least every 6 months) and higher-resolution imaging modalities (MRI over ultrasound) for higher-risk patients. CONCLUSION: Despite several published guidelines, existing evidence regarding optimal surveillance strategies in localised primary UM is of variable quality, relying on cohort studies and limited by heterogeneity, as assessed by the modified Newcastle-Ottawa Scale. There is a clear need to further define local practices and outcomes to direct future guidelines.
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 | 0.015 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".