Risk factors for survival following recurrence after first liver resection for colorectal cancer liver metastases
Bibliographic record
Abstract
BACKGROUND: Management of recurrence following liver resection for colorectal cancer metastases is a topic of debate. We determined risk factors for survival following recurrence after liver resection. METHODS: Long-term follow-up of patients in the PETCAM trial who had recurrence following liver resection. Risk groups were created according to their survival risk. Differences in overall survival (OS) between groups were estimated. Disease-free survival (DFS), patterns of disease recurrence and management were determined. Cox proportional hazard models, Kaplan-Meier method, and the log-rank test were used. RESULTS: Among 368 patients who underwent liver resection, 264 (72%) experienced disease recurrence (51% lung and 41% liver). Following liver resection, DFS: 17 months (95% CI, 14-19); OS: 57 months (95% CI, 46-70). In those who recurred, 120 (45%) received chemotherapy only, and 112 (42%) underwent second surgical resection. Among patients who experienced recurrence (n = 264), the high-risk group (more than one site of recurrence or disease-free duration < 5 months and node-positive disease) had median OS: 19 months (95% CI, 15-23) vs 36 months (95% CI, 30-48) for patients in the low-risk group (HR = 2.9, 95% CI, 2.2-3.9). CONCLUSION: Recurrence following liver resection is common. Following recurrence after liver resection, patients should be carefully selected for surgical re-resection based on risk factors.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".