Shifting incidence and survival of epithelial ovarian cancer (1995‐2014): A <scp>SurvMark</scp>‐2 study
Bibliographic record
Abstract
The aim of the study is to provide a comprehensive assessment of incidence and survival trends of epithelial ovarian cancer (EOC) by histological subtype across seven high income countries (Australia, Canada, Denmark, Ireland, New Zealand, Norway and the United Kingdom). Data on invasive EOC diagnosed in women aged 15 to 99 years during 1995 to 2014 were obtained from 20 cancer registries. Age standardized incidence rates and average annual percentage change were calculated by subtype for all ages and age groups (15-64 and 65-99 years). Net survival (NS) was estimated by subtype, age group and 5-year period using Pohar-Perme estimator. Our findings showed marked increase in serous carcinoma incidence was observed between 1995 and 2014 among women aged 65 to 99 years with average annual increase ranging between 2.2% and 5.8%. We documented a marked decrease in the incidence of adenocarcinoma "not otherwise specified" with estimates ranging between 4.4% and 7.4% in women aged 15 to 64 years and between 2.0% and 3.7% among the older age group. Improved survival, combining all EOC subtypes, was observed for all ages combined over the 20-year study period in all countries with 5-year NS absolute percent change ranging between 5.0 in Canada and 12.6 in Denmark. Several factors such as changes in guidelines and advancement in diagnostic tools may potentially influence the observed shift in histological subtypes and temporal trends. Progress in clinical management and treatment over the past decades potentially plays a role in the observed improvements in EOC survival.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".