S2931 Temporal Trends in the Etiology of Liver Cancer in the United States, Canada and Mexico Over the Past 3 Decades
Bibliographic record
Abstract
Introduction: Liver cancer is among the most prevalent gastrointestinal cancers and imposes a significant burden. Liver cancer has different etiologies such as alcohol-associated liver disease (ALD), hepatitis B, hepatitis C, and metabolic dysfunction-associated steatohepatitis (MASH). Therefore, evaluating the temporal trends in the etiology of liver cancer is of paramount significance to implement health policy measures. Methods: Trends in the age-standardized incidence rate (ASIR) of liver cancer due to ALD, chronic hepatitis B, chronic hepatitis C, and MASH in the United States, Mexico, and Canada for the period 1990-2019 were evaluated by retrieving data from the Global Burden of Disease database. Joinpoint analysis was performed to calculate the annual percent change (APC) and the average annual percent change (AAPC). Results: Over the period 1990-2019, an estimated total of 569,355 liver cancer cases were reported across the 3 nations. The leading etiology was hepatitis C (41.6%), followed by ALD (33.3%), hepatitis B (13.4%) and MASH (11.6%). A statistically significant incline in the ASIR of liver cancer due to hepatitis C was noted across the 3 nations with the highest increase noted in the US (AAPC 3.07, 95% CI 3.04 to 3.09, P < 0.001) followed by Canada (AAPC 1.04, 95% CI 1.01 to 1.07, P < 0.001). Similarly, liver cancer due to hepatitis B also experienced a statistically significant increase in the ASIR across the 3 countries with the highest incline observed in the US (AAPC 2.50, 95% CI 2.47 to 2.52, P < 0.001) followed by Canada (AAPC 2.45, 95% CI 2.42 to 2.48, P < 0.001) and Mexico (AAPC 0.67, 95% CI 0.61 to 0.73, P < 0.001). In regard to liver cancer due to ALD, a statistically significant increase in the ASIR was observed in the 3 nations, with the highest increase noted in Canada (AAPC 3.13, 95% CI 3.10 to 3.16, P < 0.001) followed by the U.S. (AAPC 3.10, 95% CI 3.07 to 3.13, P < 0.001) and Mexico (AAPC 1.76, 95% CI 1.72 to 1.81, P < 0.001). Liver cancer due to MASH also experienced a statistically significant incline in the ASIR with the highest increase noted in Canada (AAPC 3.51, 95% CI 3.48 to 3.52, P < 0.001) followed by the US (AAPC 3.32, 95% CI 3.29 to 3.35, P < 0.001) and Mexico (AAPC 2.01, 95% CI 1.98 to 2.05, P < 0.001). Conclusion: Overall, the U.S., Canada and Mexico experienced a worrisome significant increase in the incidence of liver cancer and its different etiologies, thus warranting prompt recognition of these findings to tackle the associated burden.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".