The impact of age on pathological insignificant prostate cancer rates in contemporary robot-assisted prostatectomy patients despite active surveillance eligibility
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to assess insignificant prostate cancer (iPCa) rates after robot-assisted radical prostatectomy (RARP) in contemporary patients who were preoperatively eligible for active surveillance (AS). iPCa indicates no risk of PCa progression. METHODS: We retrospectively analyzed 2837 RARP patients (2010-2019) who fulfilled at least one AS entry criteria set: Prostate Cancer Research International - Active Surveillance (PRIAS), University of California San Francisco (UCSF) (San Francisco, CA, USA), National Comprehensive Cancer Network (NCCN) or University of Toronto, ON, Canada. We utilized four different iPCa definitions: 1) based on pT2 and Gleason Score ≤6 and also cumulative tumor-volume; 2) ≤2.5mL; 3) ≤0.7mL; or 4) ≤0.5mL. For each AS set we tested the rates of iPCa and compared between age <70 vs. ≥70 years. This was complemented by multivariable logistic regression (LRM) predicting iPCa, adjusted for age and clinical AS variables. Finally, within the subgroup who had iPCa, we tested the rate of those who were deemed preoperatively AS ineligible. RESULTS: Between most (PRIAS) and least stringent (TORONTO) AS sets, iPCa was correctly predicted in 70-57%. Similarly, for iPCa definitions 2-4, rates were 59-42%, 34-19% and 27-14%. Senior patients harbored decreased proportions of iPCa. LRM confirmed that advanced age is associated with a lower chance of iPCa. More stringent AS sets lead to higher rates of AS ineligibility, e.g. 53% for PRIAS, despite iPCa. CONCLUSIONS: AS sets show limited accuracy for stricter iPCa definitions, which further declined with advanced age. Greater AS stringency resulted in more AS ineligible patients despite harboring iPCa. In consequence, patients are at risk for overtreatment. Clinicians must consider age and different AS sets that result in highly variable detection rates of iPCa.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".