Prognostic factors for first-line therapy and overall survival of metastatic uveal melanoma: The Princess Margaret Cancer Centre experience
Bibliographic record
Abstract
Metastatic uveal melanoma (MUM) has a poor prognosis, with no established standard of care. Delineation of prognostic factors in MUM patients may enable stratified treatment algorithms of stage-specific survival. Overall, 132 MUM patients who presented to a single tertiary institution in Toronto, Canada, over 12 years were identified and data (demographics, clinical status, radiographic images, and laboratory values) were extracted. Associations with systemic first-line treatment outcome 12 weeks after first-line treatment, time to progression (TTP), and overall survival (OS) were explored by univariate and multivariable analysis. Age, presence of liver metastases, and time from primary presentation to metastatic presentation were significant variables affecting first-line treatment outcomes. Age, Eastern Cooperative Oncology Group (ECOG) score, presence of liver metastases, liver metastasis size, neutrophil lymphocyte ratio, absolute neutrophil count, lactate dehydrogenase (LDH), alkaline phosphatase, time from primary presentation to metastatic presentation, and patients receiving surgery as the first-line treatment were significant variables affecting TTP. Age, ECOG score, presence of liver metastases, liver metastasis size, neutrophil lymphocyte ratio, absolute neutrophil count, LDH, and alkaline phosphatase were significant variables affecting OS. Patients who underwent surgery, chemotherapy, immunotherapy, liver-directed therapy, or targeted therapy had better OS compared with patients not receiving treatment with surgery, associated with a significantly better OS compared with all other therapies. Multivariable analysis showed increased age, absence of liver metastases, and absence of bone metastases to be associated with positive treatment outcomes. ECOG score of at least 1, increased LDH, and decreased time from primary to metastatic presentation would predict decreased TTP. Increased LDH, older age, and ECOG score of at least 1 were associated with decreased OS. These results identified prognostic markers and models thereof of treatment benefit and survival. Further validation in larger cohorts is required.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".