Metastatic conjunctival melanoma: a multicentre international study
Bibliographic record
Abstract
BACKGROUND: To reveal clinical findings related to metastatic conjunctival melanoma. METHODS: 10 ophthalmic oncology centres (9 countries and 4 continents) shared data to create a large clinical case series. The main outcome measures were the incidence and cumulative risk of systemic metastasis, study mortality rates and Kaplan-Meier patient mortality after developing conjunctival melanoma metastasis. RESULTS: Of 288 patients, 29 developed metastasis. Five had metastasis at presentation, were American Joint Committee on Cancer (AJCC) cT3-category and exhibited tumour-surface ulceration. Four of five (80%) had melanotic tumours with plical and/or caruncular involvement and died within 1 year. One survived 21 months. In contrast, 24 developed metastases during follow-up (mean 4.6±3.2 years). Their primary tumours were cT1 (n=13/24, 54.1%), cT2 (n=6/24, 25%), cT3 (n=2/24, 8.3%) and 3 cTx (12.5%) at presentation. Death had occurred in 17 patients (n=17/24, 71%) by the end of the study. The cumulative risk of systemic metastasis after treatment was 0.4% (95% CI 0.6% to 2.9%) at 1 year, 8.6% (95% CI 5.1% to 14.3%) at 5 years and 22.3% (95% CI 14.5% to 33.5%) at 10 years. Each increase in AJCC cT category was associated with an 89% higher risk for metastasis (HR=1.89, p<0.001). Among all 29 patients who developed metastasis, those who presented with AJCC cT3 disease were at highest risk (p<0.001). Liver and lung (n=13 each) were the most reported metastatic sites. CONCLUSION: Metastatic conjunctival melanoma was found in 10% of conjunctival melanoma patients. Tumour-specific characteristics including AJCC cT3-category, conjunctival location and surface ulceration were associated with metastatic risk. Survival durations were shorter for those presenting with metastasis.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".