MP78-03 TUMOR CONTROL OUTCOMES OF SALVAGE CRYOTHERAPY FOR RADIORECURRENT PROSTATE CANCER AT MEDIAN 12 YEARS FOLLOW-UP
Bibliographic record
Abstract
INTRODUCTION AND OBJECTIVES: Primary whole gland cryotherapy has been shown to be an effective treatment for clinically localized prostate cancer with multiple RCTs demonstrating similar rates of biochemical recurrence (BCR) compared to radiation therapy.While several studies have assessed risk factors of BCR after radical prostatectomy this data is limited for patients undergoing primary whole gland cryotherapy.We therefore sought to determine specific disease, perioperative and early postoperative variables that modify risk of BCR.METHODS: We performed a retrospective analysis of patients who received primary whole gland cryotherapy between 2007 and 2017 at a large tertiary referral center.The primary outcome was BCR, defined as per the Phoenix criteria (PSA nadir þ 2.0 ng/ml).Cox proportional hazard regression analysis was used to test models of variables predicting BCR.The Akaike information criteria (AIC) method was used to model the optimal PSA nadir cut-off for risk of BCR.RESULTS: 350 of 391 patients who received cryotherapy during the study period at our institution were identified as having received primary whole gland cryotherapy.Median follow up time was 38.6 months.BCR occurred in 119 (34%) patients.Age (HR[1.05,p 0.01) and NCCN risk categories (Intermediate risk: HR[6.11, p[0.07; High risk: HR[12.3,p[0.01;Very high risk: HR[14.9, p[0.01) were found to be independently associated with increased risk of BCR.A PSA nadir 0.7 was determined to best predict BCR with PSA 0.7 increasing risk of BCR by a HR[4.36 (p 0.01).CONCLUSIONS: We have identified several robust disease specific and early postoperative predictors of BCR.These risk factors may be used in counselling patients before their cryoablation as well as for potentially selecting patients who may require closer follow-up based on PSA nadir 0.7 or higher risk features.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".