Impact of population center (PC) size on access to care in advanced hepatocellular carcinoma (HCC).
Bibliographic record
Abstract
489 Background: To evaluate access to subspecialists, local therapies, treatment at a specialized HCC center, and survival in advanced HCC patients (pts) based on geographical distribution. Methods: Retrospective chart review was performed on HCC pts who received sorafenib in British Columbia from 2008 to 2016. Pts were stratified by Statistics Canada PC size criteria: large urban PC (LUPC), medium urban PC (MUPC), and small urban PC (SUPC). Chi-square tests and Kaplan Meier were used to analyze the groups. Results: Of 288 pts, geographical distribution was: LUPC 75%, MUPC 16%, SUPC 8%, and rural 0.3%. Age, gender, and ECOG performance status were similar; a higher proportion of Asians (50 vs 9 vs 4%), Child Pugh A (93 vs 83 vs 83%), and hepatitis B (37 vs 15 vs 4%) was observed in LUPC vs MUPC and SUPC, respectively. SUPC pts were less likely to see a hepatologist (p=0.04, Table); access to other subspecialists was similar. Pts from LUPC were more likely to have transarterial chemoembolization compared to MUPC and SUPC (38 vs 20 vs 21%; p=0.04); receipt of other local therapies was similar. Sixty percent were treated at a specialized HCC center and were more likely to see a hepatologist (83 vs 19%), hepatobiliary surgeon (57 vs 42%), and/or interventional radiologist (32 vs 13%) (all p<0.01). Median OS was higher for pts treated at a HCC center (24.7 vs 13.2 mo, p<0.01), but similar when stratified by PC size (overall mOS 19.3 mo, p=0.59). Conclusions: Geography did not significantly impact access to care or survival, but pts treated at a specialized HCC center have improved survival. Further research is needed to better 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".