Does adding local salvage ablation therapy provide survival advantage for patients with locally recurrent prostate cancer following radiotherapy?
Bibliographic record
Abstract
INTRODUCTION: Some men who experience prostate cancer recurrence post-radiotherapy may be candidates for local salvage therapy, avoiding and delaying systemic treatments. Our aim was to assess the impact of clinical outcomes of adding salvage local treatment in prostate cancer patients who have failed radiation therapy. METHODS: Following radiation biochemical failure, salvage transperineal cryotherapy (sCT, n=186), transrectal high intensity focused ultrasound ablation (sHIFU, n=113), or no salvage treatment (NST, identified from the pan-Canadian Prostate Cancer Risk Stratification [ProCaRS] database, n=982) were compared with propensity-score matching. Primary endpoints were cancer-specific survival (CSS) and overall survival (OS). RESULTS: Median followup was 11.6, 25.1, and 14.3 years following NST, sCT, and sHIFU, respectively. Two propensity score-matched analyses were performed: 1) 196 NST vs. 98 sCT; and 2) 177 NST vs. 59 sHIFU. In the first comparison, there were 78 deaths and 49 prostate cancer deaths for NST vs. 80 deaths and 24 prostate cancer deaths for sCT. There were significant benefits in CSS (p<0.001) and OS (p<0.001) favoring sCT. In the second comparison, there were 52 deaths (31 from prostate cancer) for NST vs. 18 deaths (nine from prostate cancer) for sHIFU. There were no significant differences in CSS or OS possibility attributed to reduced sample size and shorter followup of sHIFU cohort. CONCLUSIONS: In select men with recurrent prostate cancer post-radiation, further local treatment may lead to benefits in CSS. These hypothesis-generating findings should ideally be validated in a prospective clinical trial setting.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".