Real-world treatment patterns, clinical outcomes, and health care resource utilization in advanced unresectable hepatocellular carcinoma
Bibliographic record
Abstract
BACKGROUND: The incidence of advanced unresectable hepatocellular carcinoma (HCC) is increasing in developed countries and the prognosis of advanced HCC remains poor. Real-world evidence of treatment patterns and outcomes can highlight the unmet clinical need. METHODS: We conducted a retrospective population-based cohort study of advanced unresectable HCC patients diagnosed in Alberta, Canada (2008–2018) using electronic medical records and administrative claims data. A chart review was conducted on patients treated with systemic therapy to capture additional information related to treatment. RESULTS: A total of 1,297 advanced HCC patients were included of whom 555 (42.8%) were recurrent cases and the remainder were unresectable at diagnosis. Median age at diagnosis was 64 (range 21–94) years and 82.1% were men. Only 274 patients (21.1%) received first-line systemic therapy and of those, 32 patients (11.7%) initiated second-line therapy. Nearly all of the patients received sorafenib (>96.4%) in first-line, and these patients had considerably higher median survival (12.23 months; 95% CI 10.72–14.10) compared with patients not treated with systemic therapy (2.66 months; 95% CI: 2.33–3.12; log-rank p value <0.001). Among patients treated with systemic therapy, overall survival was higher for recurrent cases, patients with Child-Pugh A functional status, and patients with HCV or multiple known HCC risk factors ( p <0.05). CONCLUSIONS: In a Canadian real-world setting, patients who received systemic therapy had greater survival than those who did not, but outcomes were universally poor. These results underscore the need for effective front-line therapeutic options.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".