Upgrading on radical prostatectomy specimens of very low- and low-risk prostate cancer patients on active surveillance: A population-level analysis
Bibliographic record
Abstract
INTRODUCTION: A proportion of prostate cancer (PCa) patients initially managed with active surveillance (AS) are upgraded to a higher Gleason score (GS) at the time of radical prostatectomy (RP). Our objective was to determine predictors of upgrading on RP specimens using a national database. METHODS: The Surveillance, Epidemiology, and End Results Prostate with Watchful Waiting database was used to identify AS patients diagnosed with very low- or low-risk PCa who underwent delayed RP between 2010 and 2015. The primary outcome was upgrading to GS 7 disease or worse. Logistic regression analyses were used to evaluate demographic and oncological predictors of upgrading on final specimen. RESULTS: A total of 3775 men underwent RP after a period of AS, 3541 (93.8%) of whom were cT2a; 792 (21.0%) patients were upgraded on RP specimen, with 85.4%, 10.6%, and 3.4% upgraded to GS 7(3+4), 7(4+3), and 8 diseases, respectively. On multivariable analysis, higher prostate-specific antigen (PSA) at diagnosis (5-10 vs. 0-2 ng/ml, odd ratio [OR] 2.59, p<0.001) and percent core involvement (80-100% vs. 0-20%, OR 2.52, p=0.003) were significant predictors of upgrading on final RP specimen, whereas higher socioeconomic status predicted lower odds of upgrading (highest vs. lowest quartile OR 0.75, p=0.013). CONCLUSIONS: Higher baseline PSA and percent positive cores involvement are associated with significantly increased risk of upgrading on RP after AS, whereas higher socioeconomic status predicts lower odds of such events. These results may help identify patients at increased risk of adverse pathology on final specimen who may benefit from earlier definitive treatment.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.000 |
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".