The Impact of Geography in Hepatocellular Carcinoma: A Retrospective Population Based Study
Bibliographic record
Abstract
Background: The treatment of hepatocellular carcinoma (HCC) includes different therapeutic modalities and multidisciplinary tumor board reviews. The impact of geography and treatment center type (quaternary vs. non-quaternary) on access to care is unclear. Methods: A retrospective chart review was performed on HCC patients who received sorafenib in British Columbia from 2008 to 2016. Patients were grouped by Statistics Canada population center (PC) size criteria: large PC (LPC), medium PC (MPC), and small PC (SPC). Access to specialists, receipt of liver-directed therapies, and survival outcomes were compared between the groups. Results: Of 286 patients, the geographical distribution was: LPC: 75%; MPC: 16%; and SPC: 9%. A higher proportion of Asians (51% vs. 9% vs. 4%; p < 0.001), Child–Pugh A (94% vs. 83% vs. 80%; p = 0.022), and hepatitis B (37% vs. 15% vs. 4%; p < 0.001) was observed in LPC vs. MPC vs. SPC, respectively. LPC patients were more likely referred to a hepatologist (62% vs. 48% vs. 40%; p = 0.031) and undergo transarterial chemoembolization (TACE) (43% vs. 24% vs. 24%; p = 0.018). Sixty percent were treated at a quaternary center, and the median overall survival (OS) was higher for patients treated at a quaternary vs. non-quaternary center (28.0 vs. 14.6 months, respectively; p < 0.001) but similar when compared by PC size. Treatment at a quaternary center predicted an improved survival on multivariate analysis (hazard ratio (HR): 0.652; 95% confidence interval (CI): 0.503–0.844; p = 0.001). Conclusions: Geography did not appear to impact OS but patients from LPC were more likely to be referred to hepatology and undergo TACE. Treatment at a quaternary center was associated with an improved survival.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".