The Effects of Age, Period, and Cohort on the Mortality of Cervical Cancer in Three High‐Income Countries: Canada, Korea, and Italy
Bibliographic record
Abstract
BACKGROUND: As the second most common gynecologic cancer worldwide, cervical cancer has led to morbidity and mortality in thousands of women. Our study is aimed at comparing the long-term trends of mortality rates for cervical cancer in three high-income countries-Canada, Korea, and Italy-and analyzing the detached effects of chronological age, time period, and birth cohort by age-period-cohort (APC) analysis. METHODS: Joinpoint regression was used in this study, and the age-period-cohort model combined with the intrinsic estimator method was also applied to estimate the detached effect of each age, time period, and birth cohort on cervical cancer mortality. RESULTS: For the overall trends of ASMRs for cervical cancer, the rates for Canada and Italy generally decreased during the whole observation periods while the rate for Korea exhibited a significant increase from 1986 to 2003. The APC analysis suggested that the cancer mortality risks consistently increased with age in the age groups including women aged 20 to 50 years in all areas. The period effect exhibited a general upward trend for both Korea and Italy, while a decreased trend was observed for Canada during the whole observation period. The mortality risk generally decreased with birth cohort, except there was a slight increase for younger generations in the three countries. CONCLUSIONS: Our study shows that the overall decrease in the cohort effect may have contributed to the reduced mortality rate for Italy and Canada, and the increased period effects and cohort effect in younger generations may have led to the increase in cancer mortality rate for Korea.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| 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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".