Partial Cancer Prevalence in Japan up to 2020: Estimates Based on Incidence and Survival Data from Population-based Cancer Registries
Bibliographic record
Abstract
Measuring cancer prevalence in Japan has been difficult because population-based cancer registries have been conducted in limited areas. The purpose of this study was to estimate cancer prevalence in Japan from 1995 to 2020 for 5-year periods based on selected population-based cancer registry data. 1-, 2-3-, 4-5- and 5-year partial prevalence were estimated using incidence and survival data. Incidence and survival were calculated using data from selected cancer registries. We estimated the cancer survival by age group, primary site, and sex using the mixture cure fraction model. Kaplan-Meier estimates were applied to subgroups for which the survival did not converge to the estimated model. We projected that 1-year cancer prevalence for all sites would increase from 209 971 to 367 354 for men and from 164 622 to 275 776 for women, that 2-3-year prevalence would increase from 288 284 to 508 731 for men and from 255 684 to 418 630 for women, that 4-5-year prevalence would increase from 216 834 to 379 461 in men and from 211 764 to 342 031 in women, and that 5-year prevalence would increase from 715 089 to 1 255 546 in men and from 632 070 to 1 036 437 in women. This study is the first estimate of cancer prevalence in the future in Japan.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| 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".