A77 TRACKING WAIT TIMES AND OUTCOMES OF RADIOFREQUENCY ABLATION IN PATIENTS WITH HEPATOCELLULAR CARCINOMA: A QUALITY IMPROVEMENT INITIATIVE
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) is the fifth most common cancer and the third leading cause of cancer death worldwide.1 Radiofrequency ablation (RFA) is the current recommended curative treatment option for non-surgical patients with early stage disease.2 A recent report by Cancer Care Ontario shows the expected number of HCC cases requiring RFA will grow at a greater rate than RFA capacity estimates.3 Currently, no recommended wait times exist for HCC. In this study, we look at wait times for patients with HCC undergoing RFA and the development of adverse events. We also aimed to identify system gaps where quality improvement measures can be implemented. This was a retrospective study conducted at the University Health Network looking at all patients diagnosed with HCC and referred for RFA between January 2010 until December 2013. Data on demographics and co-morbidities were obtained along with biochemistry, hematology and virology values. The time from diagnosis of HCC to presentation at Tumor Board rounds and the time from Tumor Board rounds to treatment were documented. Outcomes defined a priori were all-cause death and all-cause liver transplantation. Statistical analysis was performed with a help of a statistician. The study was approved by the UHN Research Ethics Board. 225 patients were included in the study. 72.4% percent were male and the median age was 63 years (SD+/-10.4). Median tumor size at diagnosis was 22 mm (SD +/-8.3), mean MELD was 8.7 (Range=7.2–11.3) and 55.6% had Barcelona stage 0. The cause of liver disease was viral hepatitis in 73% (Hepatitis B and C). The median time from HCC diagnosis to RFA treatment was 97 days (IQR 75–139). In multivariable analysis wait times per 30 days was associated with an increased risk of death (HR=1.16; 95% CI 1.08–1.25; p=<0.001). Our study demonstrates increasing wait times for RFA in patients with HCC is associated with an increased risk of death. The high wait times along with increasing requirements for RFA will place a heavy burden on already limited RFA resources. By identifying potential barriers, we hope to develop a comprehensive strategy to reduce wait times and allocate resources for future RFA treatment at UHN. None
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".