The Prostate Cancer Risk Stratification Project: Database Construction and Risk Stratification Outcome Analysis
Bibliographic record
Abstract
This investigation reports on the biochemical and clinical outcomes of a newly created pan-Canadian Prostate Cancer Risk Stratification (ProCaRS) database developed by the Genitourinary Radiation Oncologists of Canada (GUROC). GUROC ProCaRS template-compliant data on 7974 patients who underwent radiotherapy were received from 7 unique databases. Descriptive analysis, Cox proportional hazards, and Kaplan-Meier analyses were performed using American Society for Radiation Oncology (ASTRO) biochemical failure-free survival (BFFS), prostate cancer-specific survival, and overall survival. Multivariable modeling for the primary ASTRO BFFS end point showed that age, prostate-specific antigen, T stage, and Gleason score and components such as hormonal therapy, and radiation treatment (brachytherapy with better outcome than external-beam) were predictive of outcome. Kaplan-Meier analysis of the existing GUROC and new NCCN classification system both showed good separation of all clinical outcome curves. The construction of a pan-Canadian database has informed important prostate cancer radiotherapy outcomes and risk stratification.
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 | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".