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.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".