Treatment Patterns Among Patients With Advanced Prostate Cancer in Brazil: An Analysis of a Private Healthcare System Database
Bibliographic record
Abstract
Background: With the ongoing expansion of life-prolonging therapies approved to treat advanced prostate cancer, there is currently an unmet need to better understand real-world treatment patterns and identify optimal treatment sequencing for men with metastatic castration-resistant prostate cancer (mCRPC). Methods: In this retrospective, observational cohort analysis, patients with confirmed mCRPC were identified in the Auditron claims database and used to describe mCRPC treatment patterns and trends in the Brazilian private healthcare system from 2014 to 2019. Demographics and clinical characteristics, prostate cancer stage at diagnosis, and type and number of treatment lines were evaluated. The primary endpoint was identification of the drugs used in first-line therapies in mCRPC, and the secondary endpoint included a description of sequential lines of therapy (second and third lines) in mCRPC. Results: A total of 168 electronic patient records were reviewed. Docetaxel was the most frequently used first-line treatment (35.7%), followed by abiraterone (33.3%) and enzalutamide (13.1%). Docetaxel, abiraterone, and enzalutamide also accounted for 34.6%, 28.0%, and 15.0%, respectively, of second-line therapies. In third-line therapies, cabazitaxel (26.1%), enzalutamide (23.9%), docetaxel (15.2%), and abiraterone (15.2%) were most commonly prescribed. Irrespective of stage at diagnosis, treatment patterns were similar once the disease progressed to the metastatic castration-resistance stage. Conclusions: Docetaxel was the most frequently utilized therapy for mCRPC treatment, followed by abiraterone and enzalutamide. Although the current analyses provide real-world insights into treatment patterns for patients with mCRPC in Brazil, additional real-world data are needed to further validate and expand on these findings.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".