Wait Time for Curative Intent Radio Frequency Ablation is Associated with Increased Mortality in Patients with Early Stage Hepatocellular Carcinoma
Bibliographic record
Abstract
INTRODUCTION: Radiofrequency ablation (RFA) is a recommended curative intent treatment option for patients with early stage hepatocellular carcinoma (HCC). We investigated if wait times for RFA were associated with residual tumor, tumor recurrence, need for liver transplantation, or death. MATERIAL AND METHODS: We conducted a retrospective study of patients diagnosed with HCC between January 2010 and December 2013 presenting to University Health Network (UHN) in Toronto, Canada. All patients receiving curative intent RFA for HCC were included. Multivariable Cox regression was used to determine if wait times were associated with clinical outcomes. RESULTS: 219 patients were included in the study. 72.6% were male and the median age was 62.7 years (IQR 55.6-71). Median tumor size at diagnosis was 21.5 mm (IQR 17-26); median MELD was 8.7 (IQR 7.2-11.4) and 57.1% were Barcelona stage 0. The cause of liver disease was viral hepatitis in 73.5% (Hepatitis B and C). The median time from HCC diagnosis to RFA treatment was 96 days (IQR 75-139). In multivariate analysis, wait time was not associated with requiring liver transplant or tumor recurrence, however, each incremental 30-day wait time was associated with an increased risk of residual tumor (HR = 1.09; 95% CI 1.01-1.19; p = 0.033) as well as death (HR = 1.23; 95% CI 1.11-1.36; p ≤ 0.001). CONCLUSION: Incremental 30-day wait times are associated with a 9% increased risk of residual tumor and a 23% increased risk of death. We have identified system gaps where quality improvement measures can be implemented to reduce wait times and allocate resources for future RFA treatment, which may improve both quality and efficiency of HCC care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".