Prostate Cancer and Ontario: An Analysis of Overdiagnosis and Biopsy Trends Across Ontario
Bibliographic record
Abstract
Prostate Cancer is the most diagnosed cancer in Canada with 1 in 6 men being diagnosed with the disease over their lifetime. Despite the widespread use of early detection (in the form of PSA tests and prostate biopsies) previous studies have shown discrepancies in whether these efforts are effective in reducing mortality rates from the disease itself. Using data from Ontario healthcare databases, along with a survey sent to Ontario urologists, our study attempts to discern whether variations in prostate biopsy rates across Ontario correlate to prostate cancer incidence and/or mortality rates. This will provide a descriptive picture of biopsy practices across Ontario as well as shed insight on whether there is overdiagnosis occurring of prostate cancers which, if not detected, would not have affected the individual in their lifetime. The ICES database (which has access to various records like OHIP and Cancer Care Ontario) will be used to compare incidence, mortality, and biopsy rates for prostate cancer during the time period of 1994-1998 and 2003-2007. Additionally, 149 urologists in Ontario were sent a 10 scenario questionnaire attempting to discern their tendency to biopsy patients with more ambiguous test results. Final results will be available at the time of the conference. Current preliminary analysis shows large variability across Ontario. Despite being the most diagnosed cancer in Canada, there seems to be wide variation in the behavior of urologists. Future policies should aim to standardize practices to ensure best possible care of patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".