Factors Associated with Early Recurrence after Resection for Hepatocellular Carcinoma and Outcomes
Bibliographic record
Abstract
BACKGROUND: Early recurrence (ER) (<1 year) after liver resection is one of the most important factors that impact the prognosis of patients with hepatocellular carcinoma (HCC). We sought to determine factors associated with ER of HCC and examine the outcomes thereafter. STUDY DESIGN: From March 2001 to June 2003, 56 patients underwent hepatic resection for HCC at University of Toronto and were prospectively followed with median followup of 24 months. Patients with ER were compared with those who remained disease free for more than 1 year. Patient characteristics, tumor stage, and operative procedures were evaluated for their prognostic significance by univariate and multivariable analysis. Time to recurrence and time to death were analyzed using Kaplan-Meier survival curves and compared using log-rank analysis. RESULTS: The initial procedure in all patients was surgical hepatectomy. ER occurred in 21 patients (38%), 31 (55%) remained disease free for more than 1 year, and 4 (7%) were omitted from evaluation because of early (<30 days) death. Median survival after initial hepatic resection for those with ER was 27 months, and 2-year survival was 54%. There were no deaths in the group that remained disease free for more than 1 year (100% 2-year survival, p < 0.05). By multivariate analysis, vascular invasion and positive microscopic margins were significant predictors when all 4 variables were considered in the model (p < 0.05). After ER, 11 of 21 patients (52%) underwent additional therapy with significant improvement in median survival (33 months) compared with those not eligible for conventional therapy (18 months, p = 0.05). CONCLUSIONS: ER after liver resection for HCC is the leading cause of death during the first 2 years after potentially curative resection. ER will develop in approximately 75% of patients with either vascular invasion or positive margins. For patients with these predictive factors additional treatment might be advised.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".