A real-world, population-based study for the outcomes of patients with metastatic colorectal cancer to the liver with distant lymph node metastases treated with metastasectomy
Bibliographic record
Abstract
Aim: To assess the impact of metastasectomy on survival outcomes of patients with concurrent liver and distant nodal metastases. Materials & methods: Surveillance, Epidemiology, and End Results (SEER) database was accessed and patients with colorectal liver metastases (with or without distant lymph node involvement) were reviewed. Kaplan–Meier survival estimates were then used to assess the impact of the presence of distant lymph node metastases as well as the impact of metastasectomy on overall and cancer-specific survival. A propensity score matching was then conducted between patients with distant lymph node metastases who had surgery versus those who did not have surgery. Results: A total of 15,325 patients were included in the current analysis including 1603 patients who have liver and distant nodal metastases (10.5%) and 13,722 patients who have liver metastases only (89.5%). The following factors were associated with better overall survival (OS): younger age (hazard ratio [HR] with increasing age: 1.024; 95% CI: 1.022–1.025), white race (HR for African–American race vs white race: 1.233; 95% CI: 1.175–1.295), distal site of the primary (HR: 0.808; 95% CI: 0.778–0.840), absence of distant lymph nodes (HR: 0.697; 95% CI: 0.659–0.737), metastasectomy (HR for no metastasectomy vs metastasectomy: 1.954; 95% CI: 1.858–2.056). Within the postpropensity cohort, metastasectomy was associated with improved OS among patients with concurrent distant lymph node and liver metastases (median OS of 20 vs 11 months; p < 0.001). Conclusion: Metastasectomy seems to be associated with improved survival among patients with concurrent lymph node and liver metastases. It is unclear if improved survival is related to the surgical intervention or to the fact that surgically treated patients have a better baseline general condition and hence improved outcomes.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".