Premature mortality due to cervical and ovarian cancers in Japan, 2000 to 2020
Bibliographic record
Abstract
AIM: Using the national Japanese mortality data, we investigated whether there has been an improvement in the lifespan among Japanese women who died from cervical and ovarian cancers from the years 2000 through 2020. METHODS: The number of deaths due to cervical and ovarian cancers in Japan was obtained from the World Health Organization mortality database. We calculated age standardized rates (ASR) using the direct method adjusted to the World Standard Population. Years of life lost (YLL) due to those cancers were calculated using Japanese life tables. Average lifespan shortened (ALSS) measure was calculated as a ratio of YLL to the expected lifespan. We used the bootstrap method to calculate the 95% confidence interval (95% CI) for the ALSS measure. RESULTS: The ASR for death remained mostly stable over the study at about two deaths per 100 000 women for cervical cancer, and three deaths for ovarian cancer. The ALSS values report that women who died from cervical cancer lost on average 28.3% of their lifespan (95% CI: 27.7-28.9) in 2000 and 26.6% (26.1-27.2) in 2020. Women who died from ovarian cancer lost on average 26.9% (26.5-27.4) and 23.5% (23.1-23.9) of their lifespan in 2000 and 2020, respectively. CONCLUSION: The ALSS results show that over a 20-year period, women who died of cervical and ovary cancers in Japan had their lifespans prolonged by about two and three percentage points, respectively.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| 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".