New Insights into the Epidemiology of Prostate Cancer in Ontario
Bibliographic record
Abstract
The epidemiology of prostate cancer (PC) continues to change. We evaluated the changes in incidence, in average age at diagnosis, and in survival from 1992 to 2015 in Ontario. We compared the cumulative incidence of PC-specific and non PC-specific mortality using two algorithms for cause of death: Method 1 assigned deaths from “other cancers” to non PC-specific causes, and Method 2 assigned these cases to PC-specific mortality. There were 188,714 cases diagnosed with PC between 1992 and 2015 in Ontario. The average age at diagnosis declined from 1992 to 2008 by 0.26 year (3.1 months) annually (p p > 0.05). Between 2010 and 2015, the proportion of patients diagnosed at stage IV increased, and the proportion diagnosed at stage I decreased (p-values for trends <0.001). Overall survival significantly improved over the years. The cumulative incidence of PC-specific mortality at 5 and 10 years was 6.8 and 9.8% using Method 1, and 10.2 and 16.8% using Method 2. We observed trends toward older age and more advanced stage at PC diagnosis in Ontario. Further studies are needed to validate algorithms for estimating PC-specific mortality from administrative databases.
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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".