Real-world use of systemic therapies in men with metastatic castration resistant prostate cancer (mCRPC) in Canada
Bibliographic record
Abstract
BACKGROUND: Management of advanced prostate cancer has evolved rapidly with the availability of multiple systemic treatments such as androgen-receptor axis-targeted therapies (ARATs), taxane-based chemotherapy, radium-223, and other approaches. However, limited data exists on real-world treatment selection and clinical outcomes. This study examines the utilization and survival impact of these therapies in men with metastatic castration-resistant prostate cancer (mCRPC) in the real-world setting of Ontario, Canada. METHODS: This study was a retrospective, longitudinal, population-based study of administrative claims data between January 2016 and April 2020. Men ≥ 66 years with mCRPC receiving advanced treatment were included. Patients were indexed on the day they initiated mCRPC treatment and followed up until death or end of study period to assess treatment and survival. Multinomial regression was used to model the association between baseline covariates, treatment and survival. RESULTS: Median age was 75 years among the 944 mCRPC patients who received life-prolonging therapies during this time period. Over 90% of patients used an ARAT as a first-line therapy, and 71.5% received only first-line therapy before death or censoring. Of patients that received two or more lines, over 80% received subsequent therapy with a different mechanism of action. Median overall survival was 18.9 months. CONCLUSIONS: ARATs have become the predominant first-line systemic treatment option for mCRPC patients in recent years. Notably, the majority of patients received only a single line of life-prolonging therapy after developing mCRPC. In keeping with the recognized efficacy-effectiveness gap, real-world outcomes in this cohort appear poorer than in clinical trials.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".