Time trends of drug-specific actionable adverse events among patients on androgen receptor antagonists: Implications for remote monitoring
Bibliographic record
Abstract
INTRODUCTION: In light of COVID-19, reducing patient exposure via remote monitoring is desirable. Patients prescribed abiraterone/ enzalutamide are scheduled for monthly in-person appointments to screen for adverse events (AEs). We determined time trends of drug-specific actionable AEs among users of abiraterone/enzalutamide to assess the safety of remote monitoring. METHODS: A chart review was conducted on 828 prostate cancer patients prescribed abiraterone and/or enzalutamide. Data were collected to determine time to actionable first AEs, including hypertension, elevated liver enzymes (aspartate transaminase [AST], alanine transaminase [ALT]), hyperbilirubinemia, and hypokalemia. Survival analysis was used to determine time to AEs. RESULTS: In this study, 425 and 403 patients received enzalutamide and abiraterone, respectively. In total, 25.6% of those who took enzalutamide experienced an AE, compared to 28.8% of patients on abiraterone. For patients using abiraterone and experiencing an AE, cumulative incidence of AEs at three, six, nine, and 12 months were: 67.2%, 81.9%, 90.5%, and 93.9%, respectively. Among enzalutamide users experiencing an AE, cumulative incidence of AEs at three, six, nine, and 12 months were 51.4%, 70.7%, 82.6%, and 88.1%, respectively. The AEs associated with enzalutamide were hypertension and liver dysfunction (77.1% and 22.9%, respectively). In the abiraterone group, associated AEs were liver dysfunction (47.4%), hypertension (47.4%), and hypokalemia (5.2%). CONCLUSIONS: Attaining AEs secondary to abiraterone/enzalutamide decreases over time and tends to occur within the first six months of therapy. Most actionable AEs can be remotely monitored. Given COVID-19, remote monitoring after six months of initiating abiraterone or enzalutamide appears appropriate.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".