The impact of socioeconomic status with overall survival in hepatocellular carcinoma: a systematic review and meta-analysis
Bibliographic record
Abstract
Background Hepatocellular carcinoma (HCC) is the sixth most common malignancy and the second leading cause of cancer-related mortality worldwide. Patients are typically diagnosed at an advanced stage, with a poor prognosis. Despite recent therapeutic advances in HCC, there are concerns about the accessibility of these treatments for patients of lower socioeconomic status (SES), which may lead to poorer outcomes and exacerbate health inequity. Methods We searched MEDLINE and Embase from inception to 26 April 2024 to identify cohort studies comparing SES indicators and HCC outcomes. We computed hazard ratios (HRs) with accompanying 95% confidence intervals (CIs) for each study and pooled the results using a random-effects meta-analysis. Quality assessment was carried out using the Newcastle–Ottawa Quality Assessment Scale. Results In this meta-analysis of 15 studies involving 199 404 patients, we analysed the impact of SES on overall survival in patients with HCC. Lower income (HR 1.10, 95% CI 1.02-1.18, P < 0.01) and lower insurance coverage (HR 1.22, 95% CI 1.12-1.32, P = 0.01) had a negative impact on overall survival from HCC, but we were unable to stratify by stage. Absent or reduced insurance status had a negative impact on overall survival from HCC, regardless of the stage of HCC (early-stage HR 1.18, 95% CI 1.03-1.36 versus all-stage HR 1.25, 95% CI 1.13-1.38). Conclusion Lower income and insurance coverage negatively impacted overall survival from HCC. Absent or reduced insurance status negatively impacted overall survival from HCC, regardless of the stage of HCC. Further studies from low- to middle-income and Asian countries are needed. There is an urgent need to develop policies to reduce the survival gap between patients with HCC of differing SES status.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.040 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".