Active Treatments Prolong the Survival in Patients With Hepatocellular Carcinoma and Performance Status 3 or 4
Bibliographic record
Abstract
GOALS AND BACKGROUNDS: Best supportive care is suggested as the standard treatment for hepatocellular carcinoma (HCC) patients with performance status (PS) 3-4 by the Barcelona Clinic Liver Cancer (BCLC) system. To investigate the rationale of treatment allocation. STUDY: A total of 2660 HCC patients were reviewed. One-to-one matched pairs between PS 3 and 4 patients receiving supportive care and anti-HCC treatments were generated by using the propensity score with matching model. The survival analysis was performed with the Kaplan-Meier method and log-rank test. The hazard ratio was calculated with the Cox proportional hazards model. RESULTS: Among 328 patients with PS 3-4, 38% of patients received active anti-HCC treatments against the BCLC system. Compared with patients undergoing supportive care, patients receiving anti-HCC treatments more often had milder cirrhosis, smaller tumor burden, and lower serum α-fetoprotein levels (all P<0.05). Patients undergoing supportive care had significantly decreased survival (P<0.0001). With propensity scores, 101 pairs of similar HCC patients with PS 3-4 were selected from different treatment groups. They were comparable in age, sex, etiologies of liver disease, severity of cirrhosis, tumor burden, and prevalence of diabetes mellitus (all P>0.05) at baseline. In the matching model, patients with PS 3-4 undergoing supportive care had significantly shortened survival with an adjusted hazard ratio of 4.711 (confidence interval: 3.041-7.297, P<0.0001). CONCLUSIONS: Over one-third of patients with PS 3-4 receive active anti-HCC treatments against the BCLC allocation algorithm in this study. Active anticancer therapies rather than best supportive care should be performed if there is no apparent contraindication.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".