Systematic review and meta-analysis of liver resection for metastatic melanoma
Bibliographic record
Abstract
BACKGROUND: The multidisciplinary management of metastatic melanoma now occasionally includes major hepatic resection. The objective of this work was to conduct a systematic review of the literature on liver resection for metastatic melanoma. METHODS: MEDLINE, Embase, the Cochrane Library and Scopus were searched (1990 to December 2012). Studies with at least ten patients undergoing liver resection for metastatic melanoma were included. Data on the outcomes of overall survival (OS) and/or disease-free survival (DFS) were abstracted and synthesized. Hazard ratios (HRs) were derived from survival curves and subjected to meta-analysis using random-effects models. RESULTS: Twenty-two studies involving 579 patients (13 per cent weighted resection rate) who underwent liver resection were included. Study quality was poor to moderate. Median follow-up ranged from 9 to 59 months. Median DFS ranged from 8 to 23 months, and median OS ranged from 14 to 41 months (R0, 22-66 months, R2, 10-16 months; R0 versus R1/R2: HR 0.52, 95 per cent confidence interval (c.i.) 0.37 to 0.73). The OS rate was 56-100 per cent at 1 year, 34-53 per cent at 3 years and 11-36 per cent at 5 years. Median OS with non-operative management ranged from 4 to 12 months. Comparison of OS with resection and non-operative management favoured resection (HR 0.32, 95 per cent c.i. 0.22 to 0.46). CONCLUSION: Radical resection of liver metastases from melanoma appears to improve overall survival compared with non-operative management or incomplete resection, but this observation requires future confirmation as selection bias may have confounded the results.
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 | 0.019 | 0.048 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.035 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".