Management of Colorectal Liver Metastases in Older Patients: a Decision Analysis
Bibliographic record
Abstract
BACKGROUND: The incidence of liver metastases from colorectal cancer (CLM) is on the rise. Older cancer patients are frequently subject to under-treatment. METHODS: A Markov decision model was built to examine the effect on life expectancy (LE) and quality-adjusted life expectancy (QALE) of four strategies – best supportive care (BSC), systemic chemotherapy (SC), radiofrequency ablation (RFA), and hepatic resection (HR). The model was designed to account for both age and comorbidities. RESULTS: In the base case analysis, BSC, SC, RFA, and HR yielded LEs of 11.9, 23.1, 34.8, and 37.0 months, respectively, and QALEs of 7.8, 13.2, 22.0, and 25.0 months, respectively. Model results were sensitive to several variables including age, comorbidity status, and length of model simulation. CONCLUSION: Hepatic resection may be the optimal treatment strategy for healthy older patients with CLM. Treatment decisions in older cancer patients should be individualized and account for patient age, comorbidities, and values.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".