Treatment patterns and survival in hepatocellular carcinoma in the United States and Taiwan
Bibliographic record
Abstract
BACKGROUND: Survival in hepatocellular carcinoma (HCC) is lower in the USA than in Taiwan. Little is known about the extent to which differences in stage at diagnosis and treatment contribute to this difference. We examined treatment patterns and survival in HCC and analyzed factors driving the difference. METHODS: Using a uniform methodology, we identified patients aged 66 years and older with newly diagnosed HCC between 2004 and 2011 in the USA and Taiwan. We compared treatment within 6 months after HCC diagnosis and 2-year stage-specific survival between the two countries. RESULTS: Compared with patients in Taiwan (n = 32,987), patients in the USA (n = 7,003) were less likely to be diagnosed as stage IA (4% vs 8%) and II (13% vs 22%), or receive cancer-directed treatments (41% vs 58%; all p < .001). Stage-specific 2-year survival rates were lower in the USA than in Taiwan (stage IA: 57% vs 77%; stage IB: 38% vs 63%; stage II: 40% vs 57%, stage III: 14% vs 18%; stage IV: 4% vs 5%, respectively; all p < .001 except p = .018 for stage IV). Differences in age and sex (combined), stage, and receipt of treatment accounted for 3.8%, 17.0%, and 16.8% of the survival difference, respectively, leaving 62.5% unexplained. CONCLUSIONS: Differential stage at diagnosis and treatment were substantially associated with the survival difference, but approximately two-thirds of the difference remained unexplained. Identifying the main drivers of the difference could help improve HCC survival in the USA.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".