Evaluating the Impact of Common Non-Oncologic Medication Use During Radiotherapy in Patients with High-Risk Prostate Cancer
Bibliographic record
Abstract
INTRODUCTION: The treatment efficacy of prostate cancer (PCa) radiotherapy (RT) can be inadvertently affected by the concurrent usage of non-oncologic medications. Many studies have associated the intake of several non-oncologic drugs with cancer specific outcomes. In this study, we report the impact of daily non-oncologic medications including aspirin, metformin, and statins on time to progression for patients with high-risk PCa. METHODS: Patients with high- and very high risk PCa (NCCN definition of Gleason score ≥ 8, prostate-specific antigen (PSA) ≥ 20, or ≥cT3a) who received definitive RT at two institutions were included in this analysis. Progression was defined as either biochemical (PSA > nadir + 2 ng/mL), locoregional (prostate or lymph nodes, biopsy-proven), or development of distant metastases. Progression-free survival (PFS) was defined as the time elapsed from the start of RT to progression or last follow-up. Cox proportional hazards models evaluated the associations between non-oncologic medications and PFS. RESULTS: There were 237 patients eligible for this analysis, of which 47 (19.8%) and 178 (75.1%) had at least clinical T3 disease or at least Gleason 8 disease, respectively. During RT, 82 (34.6%), 88 (37.1%), and 29 (12.2%) patients were taking aspirin, statin, or metformin, respectively. Overall, 54 patients (22.8%) experienced disease progression. Neither aspirin nor statin usage had a significant association with PFS. Patients prescribed metformin displayed worse PFS compared to patients not taking metformin (aHR: 2.46, 95% CI: 1.06-5.72). CONCLUSIONS: Aspirin and statin usage was not associated with likelihood of progression in this large cohort of patients with high-/very high risk PCa. Metformin use was associated with poorer PFS, albeit with a small event rate due to fewer patients taking metformin. Further studies are needed to clarify the impact of common non-oncologic medication use on outcomes for patients with high-risk PCa.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".