Influence of Socioeconomic Status on Survival of Hepatocellular Carcinoma in the Ontario Population; A Population-Based Study, 1990–2009
Bibliographic record
Abstract
BACKGROUND: Research has shown that people from higher socioeconomic status (SES) have better hepatocellular carcinoma (HCC) survival outcomes, although no such research has been carried out in Canada. We aimed to assess if an association between SES and HCC survival existed in the Canadian context. METHODOLOGY/PRINCIPAL FINDINGS: We conducted a population-based cohort study linking HCC cases identified in the Ontario Cancer Registry between 1990 and 2009 to administrative and hospital data. Logistic regression and chi-squared tests were used to evaluate associations between SES (income quintile) and covariates. The Kaplan-Meier method was used to estimate survival. Sequential analysis of the proportional-hazards models were used to determine the association between SES and HCC survival controlling for potential prognostic covariates. During the period 1990-2009, 5,481 cases of HCC were identified. A significant association was found between SES and curative treatment (p = 0.0003), but no association was found between SES and non-curative treatment (p = 0.064), palliative treatment (p = 0.680), or ultrasound screening (p = 0.615). The median survival for the lowest SES was 8.5 months, compared to 8.8 months for the highest SES group. The age- and sex-adjusted proportional-hazards model showed statistically significant difference in HCC survival among the SES groups, with hazard ratio 0.905 (95% confidence intervals 0.821, 0.998) when comparing highest to lowest SES group. Further adjustments indicated that potentially curative treatment was the likely explanation for the association between SES and HCC survival. CONCLUSIONS/SIGNIFICANCE: Our findings suggest that a 10% HCC survival advantage exists for the higher SES groups. This association between SES and HCC survival is most likely a reflection of lack of access to care for low SES groups, revealing inequities in the Canadian healthcare system.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".