Real-World Treatment Patterns, Clinical Outcomes, Healthcare Resource Utilization, and Costs in Advanced Hepatocellular Carcinoma in Ontario, Canada
Bibliographic record
Abstract
The therapeutic landscape for aHCC has evolved in recent years, necessitating a comprehensive analysis of treatment patterns, clinical outcomes, HCRU, and costs to contextualize emerging treatments. This study aimed to investigate these outcomes using real-world data from Ontario, Canada. This retrospective cohort study was conducted using linked administrative databases from April 2010 to March 2020. Patients diagnosed with aHCC were included, and their clinical and demographic characteristics were analyzed, as well as treatment patterns, survival, HCRU, and economic burden. Among 7322 identified patients, 802 aHCC patients met the eligibility criteria for inclusion in the study. Treatment subgroups included 1L systemic therapy (53.2%), other systemic treatments (4.5%), LRT (9.0%), and no treatment (33.3%). The median age was 66 years, and the majority were male (82%). The mOS for the entire cohort from diagnosis was 6.5 months. However, patients who received 1L systemic therapy had an mOS of 9.0 months, which was significantly higher than the other three subgroups. The mean cost per aHCC-treated patient was $49,640 CAD, with oral medications and inpatient hospitalizations as the largest cost drivers. The results underscore the need for the continuous evaluation and optimization of HCC management strategies in the era of evolving therapeutic options.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".