Factors influencing recurrence following initial hepatectomy for colorectal liver metastases
Bibliographic record
Abstract
BACKGROUND: Data on recurrence patterns following hepatectomy for colorectal liver metastases (CRLMs) and their impact on long-term outcomes are limited in the setting of modern multimodal management. This study sought to characterize the patterns of, factors associated with, and survival impact of recurrence following initial hepatectomy for CRLMs. METHODS: A retrospective cohort study of patients undergoing initial hepatectomy for CRLMs at 39 institutions (2006-2013) was conducted. Kaplan-Meier methods were used for survival analyses. Overall survival landmark analysis at 12 months after hepatectomy was performed to compare groups based on recurrence. Multivariable Cox and regression models were used to determine factors associated with recurrence. RESULTS: Among 2320 patients, tumours recurred in 47·4 per cent at median of 10·1 (range 0-88) months; 89·1 per cent of recurrences developed within 3 years. Recurrence was intrahepatic in 46·2 per cent, extrahepatic in 31·8 per cent and combined intra/extrahepatic in 22·0 per cent. The 5-year overall survival rate decreased from 74·3 (95 per cent c.i. 72·2 to 76·4) per cent without recurrence to 57·5 (55·0 to 60·0) per cent with recurrence (adjusted hazard ratio (HR) 3·08, 95 per cent c.i. 2·31 to 4·09). After adjusting for clinicopathological variables, prehepatectomy factors associated with increased risk of recurrence were node-positive primary tumour (HR 1·27, 1·09 to 1·49), more than three liver metastases (HR 1·27, 1·06 to 1·52) and largest metastasis greater than 4 cm (HR 1·19; 1·01 to 1·43). CONCLUSION: Recurrence after CRLM resection remains common. Although overall survival is inferior with recurrence, excellent survival rates can still be achieved.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".