Patterns of care for non‐metastatic castration‐resistant prostate cancer: A population‐based study
Bibliographic record
Abstract
Objectives: To describe patterns of practice of PSA testing and imaging for Ontario men receiving continuous ADT for the treatment of non-metastatic castration-resistant prostate cancer (nmCRPC). Patients and Methods: This was a retrospective, longitudinal, population-based study of administrative health data from 2008 to 2019. Men 65 years and older receiving continuous androgen deprivation therapy (ADT) with documented CRPC were included. An administrative proxy definition was applied to capture patients with nmCRPC and excluded those with metastatic disease. Patients were indexed upon progression to CRPC and were followed until death or end of study period to assess frequency of monitoring with PSA tests and conventional imaging. A 2-year look-back window was used to assess patterns of care leading up to CRPC as well as baseline covariates. Results: At a median follow-up of 40.1 months, 944 patients with nmCRPC were identified. Their median time from initiation of continuous ADT to CRPC was 26.0 months. 60.7% of patients had their PSA measured twice or fewer in the year prior to index, and 70.7% patients did not receive any imaging in the year following progression to CRPC. Throughout the study period, 921/944 (97.6%) patients with CRPC progressed to high-risk (HR-CRPC) with PSA doubling time ≤ 10 months, of which more than half received fewer than three PSA tests in the year prior to developing HR-CRPC, and 30.9% received no imaging in the subsequent year. Conclusion: PSA testing and imaging studies are underutilized in a real-world setting for the management of nmCRPC, including those at high risk of developing metastatic disease. Infrequent monitoring impedes proper risk stratification, disease staging and detection of treatment failure and/or metastases, thereby delaying the necessary treatment intensification with life-prolonging therapies. Adherence to guideline recommendations and the importance of timely staging should be reinforced to optimize patient outcomes.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".