Dramatic increase in prostate cancer cases by 2021
Bibliographic record
Abstract
UNLABELLED: What's known on the subject? and What does the study add? Estimates of prostate cancer cases are often based solely on changes in the age distribution of the population or on historical trends. This study also incorporates changes in screening prevalence, sensitivity screening maneuvers and lowering threshold for biopsies. OBJECTIVE: • To estimate the magnitude of increase in prostate cancer cases diagnosed in Canada by the year 2021. PATIENTS AND METHODS: • Using available evidence, the number of new prostate cancer cases expected in 2021 was estimated based on the effects of four major factors: aging population, increased prevalence of PSA screening, lowered PSA cutoff to recommend biopsy, and improved sensitivity of prostate biopsy. • These effects were combined with population data from Statistics Canada and the Canadian Cancer Statistics to estimate new prostate cancer cases. RESULTS: • The two factors with the largest effect on estimated new prostate cancers in 2021 compared with 2009 were: aging population (increase of 39%), and lowering the PSA threshold to 2.6 ng/mL before prostate biopsy (increase of 200%). • In the 'best-case' scenario, the number of new prostate cancers will only be affected by the aging population and will increase by 39% to 35,121 new cases. • In the 'most-likely' scenario, all four factors will have a combined effect to increase new cases by 201% to 76,379. CONCLUSIONS: • The aging population and lowering PSA threshold to 2.6 ng/mL have the most significant impact on estimated new prostate cancer cases in 2021. At that time, the number of new cases may triple to 76,379 cases in Canada. • Significant planning will be required to manage this considerable increase in new prostate cancers. .
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".