Strategies for Improving Access to Effective Prostate Cancer Medications (Abiraterone and Enzalutamide) in Low- and Middle-Income Countries (LMICs): A Survey Among Nigerian Health Professionals
Bibliographic record
Abstract
IntroductionThe burden of prostate cancer (PCa) is disproportionately concentrated in low- and middle-income countries (LMICs). Abiraterone and enzalutamide have improved survival rates and quality of life for men with PCa. However, cost constraints limit access to these medications due to limited insurance coverage and out-of-pocket payments. The survey assessed the current practices and opinions of Nigerian clinical oncologists and urologists regarding the use of low dose abiraterone and enzalutamide for the management of metastatic PCa.MethodsThis survey consisted of twenty multiple-choice questions, distributed via Google Forms to urologists and oncologists in Nigeria from August to November 2024. It examined current practices, awareness of effective dose reduction strategies, and opinions on their cost-effectiveness. The collected data were entered into Microsoft Excel, and responses were presented using tables and charts.ResultsA total of 104 respondents completed the survey. Among them, 37 (36%) reported that 61%-80% of their patients initially presented with advanced PCa. Additionally, 55 respondents (53%) were unaware of studies and guidelines regarding low-dose abiraterone. Furthermore, 66% of clinicians indicated that fewer than 20% of their patients could afford abiraterone, and 91 (87.5%) noted that few could afford enzalutamide. Moreover, 92 (89%) respondents believed that low-dose abiraterone would improve compliance, while 76% felt that reducing the enzalutamide dose would also enhance compliance and decrease patient costs. Sixty percent (58%) of respondents were willing to switch to low-dose abiraterone.ConclusionThe survey revealed limited awareness of landmark studies on dose-reduction strategies for abiraterone and enzalutamide. These strategies have the potential to enhance affordability and compliance in the management of advanced PCa in Nigeria.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".