Projections of primary liver cancer to 2030 in 30 countries worldwide
Bibliographic record
Abstract
Primary liver cancer (PLC) is the sixth most common cancer worldwide and the second most common cause of cancer death. Future predictions can inform health planners and raise awareness of the need for cancer control action. We predicted the future burden of PLC in 30 countries around 2030. Incident cases of PLC (International Classification of Diseases, Tenth Revision, C22) were obtained from 30 countries for 1993-2007. We projected new PLC cases to 2030 using age-period-cohort models (NORDPRED software). Age-standardized incidence rates per 100,000 person-years were calculated by country and sex. Increases in new cases and rates of PLC are projected in both sexes. The largest increases in rates are, among men, in Norway (2.9% per annum), US whites (2.6%), and Canada (2.4%) and, among women, in the United States (blacks 4.0%), Switzerland (3.4%), and Germany (3.0%). The projected declines are in China, Japan, Singapore, and parts of Europe (e.g., Estonia, Czech Republic, Slovakia). A 35% increase in the number of new cases annually is expected compared to 2005. This increasing burden reflects both increasing rates (and the underlying prevalence of risk factors) and demographic changes. Japan is the only country with a predicted decline in the net number of cases and annual rates by 2030. Conclusion: Our reporting of a projected increase in PLC incidence to 2030 in 30 countries serves as a baseline for anticipated declines in the longer term through the control of hepatitis B virus and hepatitis C virus infections by vaccination and treatment; however, the prospect that rising levels of obesity and its metabolic complications may lead to an increased risk of PLC that potentially offsets these gains is a concern. (Hepatology 2018;67:600-611).
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".