Risk factors for disease progression after post-prostatectomy salvage radiation: Long-term results of a large institutional experience.
Bibliographic record
Abstract
110 Background: Salvage radiotherapy (SRT) has been successfully used to treat recurrent prostate cancer following radical prostatectomy (RP). The objective of this study was to identify risk factors for disease progression post-SRT. Methods: Retrospective review of 719 consecutive patients who had RP and received post-operative radiation (adjuvant/SRT) for recurrent prostate cancer from 1992-2013. Disease progression was defined by a prostate specific antigen (PSA) ≥0.2 ng/ml, local recurrence, nodal failure, or distant metastases. Analysis was restricted to patients treated after 2000, when the PSA detectability threshold decreased to 0.2. Univariable and multivariable Cox regression analysis with backwards selection was performed with the following variables: demographics (age, race), pathological features (Gleason score, positive margins, pT-stage), surgery type, radiation details, hormone therapy, and pre-SRT PSA. Secondarily, we included PSA velocity and doubling-time as continuous variables in the model. Results: 384 patients received SRT after 2000, of which 152 had disease progression, with a median time to recurrence of 6.2 years (95% CI 4.1-7.6 years). Multivariable analysis results are reported in the Table. Gleason score, T-stage, seminal vesicle invasion, and pre-SRT PSA were associated with progression. Pre-SRT PSA ≤ 0.3 conferred the lowest rate of disease progression. In a secondary model, PSA kinetics was evaluated in which doubling-time was associated with progression (HR 0.98 per month increase, 95% CI 0.96-1.00; p=0.03). Conclusions: The lowest rate of disease progression was found amongst patients treated with a PSA ≤ 0.3. A shorter DT may also be a useful predictor of disease progression after SRT. [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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".