The role of hepatic resection in the treatment of hepatocellular cancer
Bibliographic record
Abstract
UNLABELLED: Current guidelines recommend surgical resection as the primary treatment for a single hepatocellular cancer (HCC) with Child's A cirrhosis, normal serum bilirubin, and no clinically significant portal hypertension. We determined how frequently guidelines were followed and whether straying from them impacted survival. BRIDGE is a multiregional cohort study including HCC patients diagnosed between January 1, 2005 and June 30, 2011. A total of 8,656 patients from 20 sites were classified into four groups: (A) 718 ideal resection candidates who were resected; (B) 144 ideal resection candidates who were not resected; (C) 1,624 nonideal resection candidates who were resected; and (D) 6,170 nonideal resection candidates who were not resected. Median follow-up was 27 months. Log-rank and Cox's regression analyses were conducted to determine differences between groups and variables associated with survival. Multivariate analysis of all ideal candidates for resection (A+B) revealed a higher risk of mortality with treatments other than resection. For all resected patients (A+C), portal hypertension and bilirubin >1 mg/dL were not associated with mortality. For all patients who were not ideal candidates for resection (C+D), resection was associated with better survival, compared to embolization and "other" treatments, but was inferior to ablation and transplantation. CONCLUSIONS: The majority of patients undergoing resection would not be considered ideal candidates based on current guidelines. Not resecting ideal candidates was associated with higher mortality. The study suggests that selection criteria for resection may be modestly expanded without compromising outcomes, and that some nonideal candidates may still potentially benefit from resection over other treatment modalities.
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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 | 0.004 |
| 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".