Abstract 39: Hepatic Cancer in Northeastern Brazil: Regional Disparities, Underreporting, and Impact on Incidence and Mortality
Bibliographic record
Abstract
Abstract Purpose: To analyze regional disparities in the incidence and mortality of hepatic cancer (HC) in Northeastern Brazil (NE), assessing potential underreporting and the impact of regional inequalities on access to diagnosis and treatment. Methods: This descriptive epidemiological study (2021–2023) utilized data extracted from the Cancer Care Dashboard (Painel-Oncologia), the Mortality Information System (SIM), the Notifiable Diseases Information System (SINAN), the Hospital Information System (SIH), and the National Cancer Institute (INCA). We calculated the ratio of deaths to new cases, incidence and mortality rates, and the discrepancy between hospital records and population-based estimates. We also evaluated in-hospital lethality by examining the proportion of deaths among hospital admissions. Results: During the study period, approximately one quarter of in-hospital deaths from HC in Brazil occurred in the NE, a figure linked to regional determinants including a high prevalence of hepatitis B/C (12% of records), which are major risk factors for the disease. The region accounted for over 25% of hospital admissions for hepatic disease, indicating a substantial disease burden. Conversely, the NE presented the lowest in-hospital mortality rate for HC (21.73/100,000) among all regions. However, this does not translate to lower overall lethality, as the NE’s general mortality rate (4.44) is the country’s third highest. Moreover, the incidence of HC in the NE (5.07) exceeds the national average (4.29) by 18%. According to the Cancer Care Dashboard (ICD-10 C22), 2,131 new hospital cases of HC were recorded in the NE between 2021 and 2023, while 7,960 in-hospital deaths occurred in the same period—equivalent to 3.7 in-hospital deaths per new case. Nationally, INCA estimates 10,700 new annual cases of HC, whereas hospital records indicate a yearly average of only 3,572 new cases, suggesting underreporting. Conclusion: These findings highlight regional disparities in the burden of HC, with the NE accounting for a large share of deaths and a higher-than-average incidence. The high death-to-new-case ratio suggests late-stage diagnosis. The discrepancy between INCA’s projections and hospital records reinforces the likelihood of underreporting, undermining screening and early detection policies. Expanding access to timely diagnosis and treatment is vital to lower mortality rates and reduce regional inequalities in tackling HC in Brazil. Citation Format: Jéssica M. Bohnenberger, Ana Lúcia S. Rosson, Gabriela G. Dal Alba, Júlia Dorcinio, Luiza N. Candanedo, Mariana S. Afonso, Rafaela C. Pires, Yasmin M. Loureiro. Hepatic Cancer in Northeastern Brazil: Regional Disparities, Underreporting, and Impact on Incidence and Mortality [abstract]. In: Proceedings of the 13th Annual Symposium on Global Cancer Research; 2025 Sep 16. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2025;34(12_Suppl):Abstract nr 39.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".