Treatment of hepatocellular carcinoma (HCC) after sorafenib (S) over the last 10 years.
Bibliographic record
Abstract
438 Background: Until recently there were no standard treatments for HCC patients after S. This study characterizes subsequent treatments (STx) received by HCC patients over the past 10 years and assesses their impact on survival. Methods: HCC patients treated with S between 01/2008 – 06/2017 in British Columbia, Alberta, and two cancer centers in Toronto, Ontario, Canada (Princess Margaret and Sunnybrook Cancer Centre) were included. Clinical, pathologic, laboratory, treatment, and outcome data were collected. The Kaplan-Meier method was used to assess overall survival (OS) based on STx, and stratified according to a better prognostic group (BPG), defined as ECOG 0-1 and CP-A, and worse prognostic group (WPG), defined as ECOG≥2 or CP-B/C. Results: A total of 730 patients were identified. 177 (24.2%) received STx (table). Patients who received STx had longer median OS (mOS) than those who had no further treatment (12.1 vs. 3.3 months; p < 0.001). For patients treated with localized, systemic, or palliative radiation treatment, mOS was 16.8, 10.5 and 8.6 months, respectively (p < 0.001). After S, there were 206 (30.7%) patients in the BPG and 464 (69.3%) in the WPG. BPG patients were more likely to receive STx compared to WPG patients (60.5% vs. 39.5%, p < 0.001). BPG patients who received STx had better mOS than those who did not (15.9 vs. 7.0 months; p < 0.001). WPG patients also had better mOS if they received STx compared to those who did not (6.0 vs. 2.6 months; p < 0.001). Conclusions: Only a small proportion of HCC patients received subsequent treatment after sorafenib. This is likely due to poor performance status, liver dysfunction, or lack of treatment options. Patients who received subsequent treatment had improved mOS, regardless of whether they were in the better or worse prognostic group. [Table: see text]
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".