Low-Dose Abiraterone in Metastatic Prostate Cancer: Is It Practice Changing? Facts and Facets
Bibliographic record
Abstract
PURPOSE: It is projected that approximately 50,000 new cases of prostate cancer will be diagnosed in 2020 in India. Survival has improved because of the development of effective drugs such as abiraterone acetate, but universal accessibility to treatment is not always possible because of cost constraints in lower- and middle-income countries. Recently, the National Comprehensive Cancer Network (NCCN) has included low-dose abiraterone (250 mg/day) with food as an alternative treatment option to full-dose abiraterone (1,000 mg/day) fasting. METHODS: The Science and Cost Cancer Consortium conducted a survey to evaluate the use of abiraterone in India and the opinions of medical oncologists about using low-dose treatment. Modeling was used to estimate potential financial benefits to individual patients and to estimate overall costs of health care in India if low-dose abiraterone is prescribed. RESULTS: Of 251 Indian medical oncologists who were invited to participate in the survey, 125 provided their e-mail address and received the survey; 118 responded (47% of the total). Of these, 25% were not aware of the recent NCCN recommendation, 55% were already prescribing low-dose abiraterone when resources were limited, 7% had already changed their practice, and 29% agreed to switch to a universal practice of using low-dose abiraterone with food; 9% of practitioners would not use low-dose abiraterone. Estimated mean per patient savings was US$3,640, with annual savings of US$182 million in India. CONCLUSION: Use of lower-dose abiraterone would increase access to treatment in India and globally and lead to large cost savings.
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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".