Impact of Age of Onset on Survival after Hepatectomy for Patients with Colorectal Cancer Liver Metastasis: A Real-World Single-Center Experience
Bibliographic record
Abstract
PURPOSE: The incidence of early-onset CRC is increasing. However, the effect of age of onset on the long-term outcome of colorectal cancer liver metastasis (CRLM) remains unclear. This study aimed to evaluate the association between the age of onset and the oncological outcome of CRLM patients and to investigate whether the prognostic role of RAS mutation is altered with age. METHODS: We retrospectively investigated consecutive patients at our institution who underwent initial liver resection between 2006 and 2020. The inverse probability of treatment weighting (IPTW) method was used to balance the confounders among early- (≤45 years; EOCRLM), intermediate- (46-70 years; IOCRLM), and late-onset (>70 years; LOCRLM) groups. The prognostic role of RAS was assessed based on age group. RESULTS: A total of 1189 patients were enrolled: 162 in the EOCRLM group, 930 in the IOCRLM group, and 97 in the LOCRLM group. No difference in disease-free survival (DFS) was found between the three groups. However, EOCRLM were more likely to develop extrahepatic and extrapulmonary metastasis and had significantly lower five-year OS rates than IOCRLM. After IPTW, EOCRLM remained a negative prognostic predictor. RAS mutations were significantly associated with worse survival than wild-type RAS in EOCRLM and IOCRLM. However, RAS mutation did not predict the prognosis of patients with LOCRLM. CONCLUSIONS: Patients with EOCRLM had a significantly lower OS than IOCRLM patients and age influences the prognostic power of RAS status. These findings may be helpful for doctors to guide the clinical treatments and develop follow-up strategies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".