Resection of colorectal carcinoma liver metastases: A population-based study in outcomes and factors associated with recurrent disease
Bibliographic record
Abstract
To assess the hepatic disease-free survival (HDFS) and overall survival (OS) of patients who underwent resection of colorectal cancer liver metastases (CRCLM) in our population, and evaluate what factors are associated with these outcomes. Patients with resected non-mucinous CRCLM between January 2013-February 2020 were retrospectively identified. Dates of diagnosis, surgery, and, if applicable, death were recorded. HDFS and OS were calculated using a census date of 24 September 2022. Separate Cox multivariate regression analyses were performed to evaluate for association between HDFS and OS and the following factors: pre-operative imaging interval (<4 weeks vs. ≥4 weeks); pre-operative imaging modality (CT only vs. MRI+CT); extrahepatic disease at time of hepatectomy (yes vs. no); tumor burden score (TBS, where TBS2 = (largest axial dimension of CRCLM)2 + (number of CRCLM)2); pT and pN; and neoadjuvant chemotherapy. 137 subjects (mean age, 61±11 years, 86 males) were included. Associations with recurrent hepatic disease were found with chemotherapy (HR 2.11[95% CI=1.13–3.92]), TBS (HR 1.30[95% CI=1.17–1.45]), MRI+CT (HR 2.12[95% CI=1.29–3.48]), and extrahepatic disease at hepatectomy (HR 2.16[95% CI=1.08–4.35]). For mortality, associations were found with TBS (HR 1.22[95% CI=1.09–1.37]), pT (HR 1.45[95% CI=1.05–2.00]), and extrahepatic disease at hepatectomy (HR 2.10[95% CI=1.31–3.36]). In our population, non-imaging related factors TBS, neoadjuvant chemotherapy, pT and presence of extrahepatic disease at time of hepatectomy were associated with HDFS and/or OS. The preoperative imaging interval and use of preoperative MRI were not associated with improved patient 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".