Selective internal radiation therapy for hepatic metastases of uveal melanoma: a systematic review
Bibliographic record
Abstract
OBJECTIVE: Uveal melanoma (UM) commonly metastasizes to the liver. Treatment usually consists of liver-directed therapies, such as selective internal radiation therapy (SIRT). This review aimed to assess the effectiveness and safety of SIRT for hepatic metastases from UM. METHODS: The study protocol is available at OSF (https://osf.io/vhyct/). EMBASE and MEDLINE were searched until July 2020, using terms related to SIRT and hepatic metastases from UM. Studies reporting outcomes of SIRT in patients with UM and at least one hepatic metastasis were included. Data on overall survival (OS), hepatic progression free survival (hPFS) or tumor response were collected. The Newcastle-Ottawa Scale (NOS) was used to assess risk of bias. RESULTS: = 9, 81.8%) were retrospective. Disease control was achieved in 170 patients (67.5%) and the median OS from time of SIRT was 12.3 months. Median hPFS was 5.4 months. Low-grade side-effects were common but serious complications were infrequent. There were two treatment-related deaths. The median NOS score was 6 (moderate risk of bias). CONCLUSION: SIRT appears to be a safe and effective treatment for patients with unresectable hepatic metastases from UM. The certainty of our results is unclear due to predominantly retrospective data with moderate risk of bias. Further prospective studies are required to explore the role of SIRT in UM. ADVANCES IN KNOWLEDGE: SIRT appears to be a safe treatment for patients with unresectable hepatic metastases from UM. Further prospective work is required.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".