Side effect management algorithms for niraparib/abiraterone acetate in prostate cancer.
Bibliographic record
Abstract
INTRODUCTION: Niraparib, a PARP1/2 inhibitor, is newly approved in combination with abiraterone acetate (AA) plus prednisone or prednisolone (niraparib/AA+P) for the treatment of adult patients with BRCA-mutated, treatment-naïve metastatic castration resistant prostate cancer (mCRPC). Detailed guidance beyond the prescribing information may be helpful in managing the side effect profile and dosing practicalities of this combination therapy. MATERIALS AND METHODS: A panel of specialists convened to design management algorithms for four common niraparib/AA+P treatment-related adverse events (AEs) in mCRPC; anemia, thrombocytopenia, hypertension, and nausea. The algorithms build on Health Canada-approved prescribing information to highlight practical considerations related to monitoring, treatment adjustment, and specialist referral to support clinical practice. RESULTS: The panel's recommendations were largely aligned with the niraparib/AA+P product monograph. Single agent AA+P followed by reintroduction niraparib/AA+P using the low dose formulation of niraparib/AA were common strategies for managing higher grade AE's. Recommendations for hypertension management were expanded to include a sequence of anti-hypertensive medication trials prior to a change in anti-cancer therapy, where feasible. CONCLUSION: These algorithms are intended to provide practical assistance to Canadian clinicians managing the most common AEs encountered with the novel combination, niraparib/AA+P, for mCRPC.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".