Global incidence and prevalence in uveal melanoma
Bibliographic record
Abstract
Purpose: The most common intraocular cancer in adults is uveal melanoma (UM). This study aimed to investigate and report the incidence and prognosis of UM in different regions of the world. Methods: We retrieved relevant data on UM from the PubMed database and analyzed its global incidence and prognosis. All data was obtained from a national population-based registry, with publication dates ranging from 2013 to 2023. Results: The incidence rates of UM vary across different regions: in the United States, rates were 5.1 per million (1993-2008) and 5.2 per million (1973-2013); in Canada, rates ranged from 3.34 per million (1992-2010) to 5.09 per million (2011-2017); in Republic of Korea, the rate was 0.42 per million (1999-2011); in New Zealand, it was 5.56 per million (2000-2020); in Australia, it was 7.6 per million (1982-2014); and in Europe, rates ranged from 3.1 to 5.8 per million (1995-2002). Among European countries, Sweden (5.6 per million (1960-2009)), Germany (6.41 per million (2009-2015)), Poland (6.67 per million (2010-2017)), and the United Kingdom (10 per million (1999-2010)). Conclusions: The most common site of occurrence for UM is in the choroid. Limited data suggest a stable trend in UM incidence rates across the included countries, but significant differences in incidence rates exist among different countries and regions, with notably lower rates in Asian countries compared to Europe, North America, and Oceania. In general, the incidence rate in males is slightly higher compared to that in females.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".