Characteristics, Treatments, and Survival of Uveal Melanoma: A Comparison between Chinese and American Cohorts
Bibliographic record
Abstract
Uveal melanoma (UM) is the most common intraocular malignant carcinoma. This study aimed to compare the clinical features, treatment modalities, and prognosis of UM patients in China with those in America over a 15-year period. In the study, 4088 American patients with primary UM from the Surveillance, Epidemiology, and End Results (SEER) database and 1508 Chinese patients from Tongren-ophthalmology Research Association of Clinical Evaluation (TRACE) were included. Univariable and multivariable analyses were performed to determine prognostic factors and propensity score matching (PSM) and sensitivity analyses were applied to adjust for confounders and identify independent prognostic factors. Chinese patients were diagnosed at a younger age (mean ± SD, 47.3 ± 12.5 years vs. 59.7 ± 14.8 years) and tumors at diagnosis were larger (diameter: 12.0 ± 3.54 mm vs. 11.3 ± 8.27 mm; thickness: 7.13 ± 3.28 mm vs. 4.91 ± 3.01 mm). Chinese patients were more likely to undergo brachytherapy than American patients. Chinese patients had better overall survival than American patients while no significant differences exhibited after adjusting for age through PSM. In conclusion, compared with American patients, Chinese patients had younger onset age, larger tumors at diagnosis and better prognosis, mainly because of their younger age.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".