Will Patients With Liver Metastasis From Aggressives Cancers Benefit From Surgical Resection?
Bibliographic record
Abstract
Background: We aimed to evaluate the outcomes of resections for liver metastases (LMs) originating from pancreatic ductal adenocarcinoma (PDAC), non-small cell lung cancer (NSCLC), and esophagus/gastric cancers (EGCs), which we label as major killers (MKs; overall survival (OS) under 10%). We hypothesized that LM resection must provide the patient with almost a year of OS postoperatively that is considered beneficial. Methods: From January 2005 to December 2020, 23 patients underwent resection for isolated LM from MKs. These patients underwent surgery after a multidisciplinary discussion about their performance status, disease evolution during prolonged medical treatment, and the existence or absence of extrahepatic metastases. Results: LM originated from an PDAC, EGC, or NSCLC in 10 patients (43%), nine patients (39%), and four patients (18%), respectively. The median delay between primary cancer and LM diagnoses was 12 months, and the median delay between LM diagnosis and liver resection was 10 months. Most patients, who had objectively responded to medical treatment (57%), had a solitary (61%) and unilobar (70%) LM. Severe morbidity and 90-day mortality rates were 13% and 4.3%, respectively. Margin-free resection was achieved in 16 patients (70%). After liver resection, the median OS was 24 months without a statistical difference when considering the primary tumor site; 1, 3-, and 5-year OS were 70%, 23%, and 23%, respectively. Conclusion: Selection based on criteria such as good clinical condition, response to treatment, and long observation period helped identify patients with LM of MKs who seemed to benefit from resection.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".