Sorafenib-Related Adverse Events in Predicting the Early Radiologic Responses of Hepatocellular Carcinoma
Bibliographic record
Abstract
BACKGROUND: Hepatocellular carcinoma (HCC) has a poor prognosis with low chemotherapeutic efficiency to medications except to sorafenib. Previous studies showed that adverse events (AEs) of sorafenib can predict therapy efficacy to HCC. The aim of the study is to evaluate the early efficacy and AEs of sorafenib therapy. METHODS: The database of HCC patients receiving sorafenib at Taichung Veterans General Hospital during the period from June 2012 to October 2016 was analyzed. All HCC cases were Barcelona Clinic Liver Cancer (BCLC) classification stage C. The early efficacy of sorafenib was classified according to the mRECIST criteria as either partial response (PR), stable disease (SD) or progressive disease (PD). Responses were recorded within 6 weeks after the start of sorafenib treatment. AEs were defined as the appearance of hand-foot skin reaction (HFSR), hypertension (HTN) and diarrhea. Exclusion criteria were poor performance status, poor drug compliance, discontinued follow-up or mortality occurring within 1 day after medication. RESULTS: From a total of 222 subjects, eight cases (3.6%) were classified as PR, 82 cases (36.9%) SD, and 132 cases (59.5%) PD. The PR group had the highest ratio of HFSR (62.4%) and hypertension (37.5%). Pooling cases of PR and SD together, the presence of HFSR adjusted odd ratio (aOR) 2.80, 95% confidence interval (CI) 1.52 - 5.16) and diarrhea (aOR 3.42, 95% CI 1.67 - 7.01) were good predictors of favorable responses to sorafenib therapy. CONCLUSIONS: HFSR and diarrhea are good predictors of early therapy efficacy to the sorafenib treatment.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".