Impact of the 6-mo Wait Policy on Transplantation, Resection, and Ablation Outcomes for Patients With Hepatocellular Carcinoma: A National Cancer Database Analysis
Bibliographic record
Abstract
BACKGROUND: The mandatory 6-mo waiting period implemented in 2015 for accruing model for end-stage liver disease exception points in patients with early-stage hepatocellular carcinoma (HCC) awaiting orthotopic liver transplantation (OLT) has been associated with improved outcomes. However, most of these findings are dependent on cohorts who have had access to the OLT waitlist, and the policy's impact on non-OLT treatment strategies (eg, liver resection, ablation) remains poorly understood. METHODS: This was a retrospective analysis of patients with early-stage HCC (T2N0M0) from the National Cancer Database from 2010 to 2021. The pre-/post-policy era was defined by HCC diagnosis before or after 2015, respectively. The Kaplan-Meier survival method and multivariable Cox proportional hazard regression were used to estimate survival. RESULTS: Among 53 928 patients, rates of OLT decreased (13.1%-7.4%), ablation increased (19.1%-25.3%), and resection remained constant (9.2% versus 9.2%) from the pre- to post-policy era ( P < 0.001 for all). OLT was associated with the highest 5-y postoperative survival (79.7%), followed by resection (63.5%) and ablation (42.9%; P < 0.001, all pairwise comparisons). Overall survival improved in the post-policy era (hazard ratio, 0.89; 95% confidence interval, 0.87-0.92), with resection having the greatest improvement in survival (hazard ratio, 0.69; 95% confidence interval, 0.62-0.77). Among all treatment modalities, time-to-intervention was not a predictor of mortality ( P > 0.05). CONCLUSIONS: Overall, the post-policy era was associated with improved outcomes in early-stage HCC. While survival outcomes between policy eras were similar for OLT or ablation, liver resection was shown to have the highest improvement in survival and remains a durable treatment option in early-stage HCC.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".