The Association between Patient Characteristics and Biochemical Recurrence after Radical Prostatectomy
Bibliographic record
Abstract
Background: Biochemical recurrence (BCR) represents the rise of prostate-specific antigen (PSA) levels after treatment with curative radical prostatectomy (RP) or radiation for prostate cancer. The objective of the current study was to test for the association between patient characteristics, namely age, body mass index (BMI), as well as prostate volume at surgery, and BCR after RP. Material and Methods: Within a tertiary care database, patients with prostate cancer treated with RP between January 2014 and June 2023 were included. Kaplan–Meier survival analyses and Cox regression models addressed BCR after RP according to patient characteristics. Results: Of 821 patients, the median age was 66 years (interquartile range [IQR] 61–71 years), BMI was 26.2 kg/m2 (IQR 24.3–28.8 kg/m2), and prostate volume was 40 cm3 (IQR 30–55 cm3). Median follow-up was 20 months. In survival analyses, the three-year BCR-free survival rates were 81 vs. 84 vs. 81% in patients aged ≤60 vs. 61–69 vs. 70 years (p = 0.1). In patients with BMI < 25.0 vs. 25.0–29.9 vs. ≥30.0 kg/m2, the three-year BCR-free survival rates were 84 vs. 81 vs. 84% (p = 0.7). In patients with prostate volume ≤40 vs. >40 cm3, the three-year BCR-free survival rates were 85 vs. 80% (p = 0.004). In multivariable Cox regression models accounting for patient and pathologic tumor characteristics and adjuvant radiation therapy, a higher prostate volume independently predicted BCR as continuous (hazard ratio 1.012, 95% confidence interval 1.005–1.019; p < 0.001), as well as categorized the variable based on the median (hazard ratio 1.66, 95% confidence interval 1.17–2.36; p = 0.005). Conversely, neither age nor BMI were significantly associated with BCR after RP. Conclusions: The higher prostate volume independently predicted BCR after RP, but not age or BMI at surgery. Consequently, patients with an elevated prostate volume should be considered for closer postoperative follow-up.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".