Risk factors for PSA bounce following radiotherapy: outcomes from a multi-modal therapy analysis.
Bibliographic record
Abstract
INTRODUCTION: To identify risk factors for PSA bounce (PSAb) and compare characteristics of prostate cancer patients treated with brachytherapy and external beam radiotherapy (EBRT). MATERIALS AND METHODS: We identified 362 patients treated for low risk prostate adenocarcinoma (D'Amico criteria) with a follow up time of at least 36 months. Patients received either: 1) EBRT 76 Gy in 38 fractions (n = 58); 2) hypofractionated EBRT, 45 Gy in 9 once-weekly fractions (n = 74); 3) seed brachytherapy (n = 230). PSAb was defined as a rise >= 0.2 ng/mL with subsequent return to baseline within the first 3 years after treatment. Univariate and multivariate logistic regression models were estimated to assess the association between clinical factors and occurrence of PSAb. RESULTS: There was no significant difference between treatment groups (p = 0.349), with an overall PSAb rate of 28.5%. Upon univariate analysis, the following were predictive of a lower PSAb rate: older age (OR = 0.96), higher PSA at diagnosis (OR = 0.87), more positive biopsy cores (OR = 0.98), and a higher Cancer of the Prostate Risk Assessment (CAPRA) score (CAPRA of 3 versus 1: OR = 0.33). Multivariate analysis confirmed the significance of fewer positive biopsy cores (OR = 0.99) and a lower CAPRA score (CAPRA 3 versus 1: OR = 0.34). These factors also predicted a shorter time to first PSAb. CONCLUSIONS: We found comparable rates of PSAb after different regimens of radiotherapy. We hypothesize that it results from late damage to healthy prostatic tissue. This idea is supported by the fact that we found that clinical factors indicative of a lower tumor burden were predictive of a PSAb.
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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 | 0.001 |
| 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 | 0.000 |
| 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".