Hepatectomy for liver metastases from gastric cancer: a systematic review
Bibliographic record
Abstract
BACKGROUND: Official guidelines recommend palliative treatments for patients with liver metastases from gastric cancer. However, many case series reported that hepatectomy for such cases is safe and effective. This systematic review compares the overall survival between hepatectomy and palliative therapy in patients with liver metastases from gastric cancer. METHODS: Two independent reviewers performed a systematic search of literature in EMBASE and PubMed, updated until 26 October 2016. The Newcastle-Ottawa score for cohort studies was used for quality assessment of included studies. RESULTS: A total of eight cohort studies involving 196 patients in the hepatectomy arm and 481 in the palliative arm were included. Median overall survival of patients in the two arms was 23.7 (range, 13.0 to 48.0) and 7.6 (range, 5.5 to 15.2), respectively. Median rates of overall survival of the two arms were 69, 40, 33 and 27, 8, 4% at 1, 2, and 3 years, respectively. Comparing with palliative therapy, hepatectomy was associated with significantly lower mortality at 1 year (odds ratio 0.17, P < 0.001) and 2 years (odds ratio 0.15, P < 0.001). Among the patients who underwent hepatectomy, Asian cohorts showed higher median rates of overall survival than Western cohorts at 1 year (76 vs. 60%), 2 years (47 vs. 30%) and 3 years (39 vs. 23%). CONCLUSIONS: Hepatectomy in the management of liver metastases from gastric cancer can be considered effective. In the elective setting, hepatectomy provides a potential alternative to palliative therapy.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".