Debulking Hepatectomy for Colorectal Liver Metastasis Conveys Survival Benefit
Bibliographic record
Abstract
(1) Background: Despite advances in surgical technique and systemic chemotherapy, some patients with multifocal, bilobar colorectal liver metastases (CRLM) remain unresectable. These patients may benefit from surgical debulking of liver tumors in combination with chemotherapy compared to chemotherapy alone. (2) Methods: A retrospective study including patients evaluated for curative intent resection of CRLM was performed. Patients were divided into three groups: those who underwent liver resection with recurrence within 6 months (subtotal debulked, SD), those who had the first stage only of a two-stage hepatectomy (partially debulked, PD), and those never debulked (ND). Kaplan–Meier survival curves and log-rank test were performed to assess the median survival of each group. (3) Results: 174 patients underwent liver resection, and 34 patients recurred within 6 months. Of the patients planned for two-stage hepatectomy, 35 underwent the first stage only. Thirty-two patients were never resected. Median survival of the SD, PD, and ND groups was 31 months, 31 months, and 19.5 months, respectively (p = 0.012); (4) Conclusions: Patients who underwent a debulking of CRLM demonstrated a survival benefit compared to patients who did not undergo any surgical resection. This study provides support for the evaluation of intentional debulking versus palliative chemotherapy alone in a randomized trial.
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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 | 0.000 |
| 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".