The impact of geography and center volume on access to care and outcomes in advanced hepatocellular carcinoma (HCC): A retrospective population based study.
Bibliographic record
Abstract
e15597 Background: Treatment of advanced HCC is complex and involves specialized multidisciplinary care. We aimed to characterize the impact of geography and center volume on access to care and outcomes in HCC patients (pts). Methods: HCC pts who received sorafenib in British Columbia from 2008 to 2016 were included. Pts were stratified by rural vs urban status (distance from cancer center), and high volume (HVC) vs lower volume (LVC) centers. Chi-square tests and Kaplan Meier were used to test for differences between groups. Results: Of 288 pts identified, median was age 62 (IQR 56-72), 81% male, 40% Asian, 82% ECOG 0/1 and 90% Child Pugh A. Hepatitis C (32%), hepatitis B (31%) and alcohol (25%) related liver disease were most common. Most pts resided within 100 km (85%) and 173 (60%) were treated at HVC. Ethnicity, liver disease etiology, ECOG and M1 disease varied by stratification (Table). Rural pts were more likely to see an internist (30% vs 16%, p=0.04); access to other subspecialists was similar (all p>0.05). HVC pts were more likely to see a hepatologist (83% vs 19%), hepatobiliary surgeon (57% vs 42%), and/or interventional radiologist (32% vs 13%) compared to LVC pts (all p<0.01). Number of specialists seen correlated with survival (36.4 vs 20.3 vs 12.6 mo for ≥ 3 vs 2 vs 1 specialist(s), p<0.01). Median OS from time of diagnosis was higher for HVC pts (24.7 vs 13.2 mo, p<0.01), but similar when stratified by distance (p=0.44) and from sorafenib initiation (p=0.66). Conclusions: HCC patients treated at a HVC are more likely to see specialized clinicians and have improved survival outcomes. Further research is needed to understand social and clinical factors that influence these findings. [Table: see text]
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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 | 0.001 | 0.001 |
| 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".