Twenty-year trends of primary liver cancer incidence rates in an urban Chinese population
Bibliographic record
Abstract
The objective of this study was to describe trends in the incidence rates of primary liver cancer in a geographically defined Chinese population. Primary liver cancer cases (N=13 685) were diagnosed between 1981 and 2000 and identified by the Tianjin Cancer Registry. Age-adjusted and age-specific incidence rates were examined in both males and females. Poisson regression was employed to assess the incidence rate trends. Crude and age-adjusted incidence rates in the study period were: 27.4/100 000 and 16.4/100 000 in males and 11.5/100 000 and 6.4/100 000 in females, respectively. While the results from Poisson regression analyses suggest statistically significant trends of declining incidence rates of primary liver cancer overall, trends were not consistent across age and sex groups. The decline in incidence was observed, for the most part, in the 40-69 age group, with a greater decrease in males. Our findings provide a new evidence of a downward trend in incidence rates of this disease in China for a period of 20 years. As the observed decline is relatively small and inconsistent across sex and age groups, a continued epidemiological observation on this condition is required.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".