Association of baseline risk factors with cardiovascular (CV) events during long-term degarelix therapy in men with prostate cancer.
Bibliographic record
Abstract
190 Background: GnRH agonists have been associated with a greater incidence of CV events in men with prostate cancer. Less is known about GnRH antagonists and CVD. We used prospective data to evaluate the relationships of baseline CV risk, dosing regimen and duration of degarelix therapy, and CV events. Methods: Data from 16 phase II/III and one extended phase III trial was pooled (n=1,704). Maintenance doses and exposure time varied between 20-480 mg and 1-66 months, respectively. Baseline CV risk was assessed based on established CVD, BMI (>30 kg/m2), smoking, hypertension, type 2 diabetes, and hyperlipidemia. Patients were stratified into 3 groups: 1) no CV risk factors (n=337), 2) ≥1 CV risk factors without CVD (n=803), 3) established CVD (n=564). Acute CV events were collected prospectively. Results: The incidence of CV events in patients with established CVD, ≥1 CV risk factors, and no CV risk factors was 20% (n=112), 8% (n=57) and 7% (n=28), respectively. Established CVD was associated with a 3.1-fold increased risk for CV events (p<0.0001), whereas presence of ≥1 CV risk factors with 1.3-fold (p=0.28). The cumulative hazard curve indicated no increases in hazards with increasing duration of treatment. Hazards of primary CV events during degarelix treatment (0.049) were not statistically different from that during the 2 years prior treatment (0.48, p=0.85). In multivariate models, age, established CV disease, high BMI and no alcohol consumption were independently associated with greater risk for CV events (Table); type 2 diabetes (p=0.13) and smoking (p=0.08) were of borderline significance. Degarelix dose and schedule were not associated with CV risk (p=0.78) in the model. Conclusions: CV events during degarelix treatment were largely confined to those with established CVD and further modulated by aging and modifiable risk factors. Rates of CV events were similar before and after degarelix treatment. [Table: see text] [Table: see text]
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".