The effect of sorafenib (S) starting dose and dose intensity on survival in patients with advanced hepatocellular carcinoma (HCC).
Bibliographic record
Abstract
400 Background: The SHARP trial showed that S improves survival in advanced HCC. In clinical practice full dose (FD) of S at 400mg bid can be difficult to tolerate and so a reduced dose (RD) is often required. The purpose of this study was to determine whether starting dose or dose intensity of S affects survival in patients with HCC. Methods: All patients treated with S for HCC in Alberta, Canada from January 2008 to July 2016 were included in this study. Patient demographics, clinical, tumor characteristics, S starting dose and dose intensity were collected and analyzed. Patients were dichotomized into starting FD or RD of S. A mean dose intensity of > 75% and < / = 75% were considered normal and reduced, respectively. Survival outcomes were assessed with Kaplan-Meier curves and compared with the log-rank test. A Cox-proportional hazard model was constructed with starting dose, dose intensity and relevant clinical and pathologic factors to assess their impact on survival. Results: A total of 156 patients were included. Median age was 63, 78% were men, 34% were East Asian, 77% were Childs-Pugh A, and the most common causes of liver disease were hepatitis B (30%) and C (30%). Most patients had EGOG performance status of 0 and 1 prior to starting S (29% and 62%, respectively). S was started at FD in 58% of patients and 50% had a dose intensity > 75%. The median survival for both starting FD and RD was 10.3 months, and not significantly different (p = 0.14).The median survival for a dose intensity > 75% vs < / = 75% was 10.7 vs 9.5 months, respectively (p = 0.76). In multivariable models that adjusted for demographic, stage, performance status and liver function, starting dose (HR 0.8 95%CI 0.5-1.2) and dose intensity (HR0.9 95% CI 0.6-1.4) were not associated with survival. Conclusions: Starting S with a RD may be a reasonable strategy for HCC, since it does not appear to impact survival. Also, dose intensity did not impact survival, suggesting that additional dose modifications may not compromise effectiveness. Though limited by small numbers, we are planning to confirm these findings in a larger, pan-Canadian dataset.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".