Comparative Analysis of Biopsy Upgrading in Four Prostate Cancer Active Surveillance Cohorts
Bibliographic record
Abstract
Background: Active surveillance (AS) is increasingly accepted for managing low-risk prostate cancer, yet there is no consensus about implementation. This lack of consensus is due in part to uncertainty about risks for disease progression, which have not been systematically compared or integrated across AS studies with variable surveillance protocols and dropout to active treatment. Objective: To compare risks for upgrading from a Gleason score (GS) of 6 or less to 7 or more across AS studies after accounting for differences in surveillance intervals and competing treatments and to evaluate tradeoffs of more versus less frequent biopsies. Design: Joint statistical model of longitudinal prostate-specific antigen (PSA) levels and risks for biopsy upgrading. Setting: Johns Hopkins University (JHU); Canary Prostate Active Surveillance Study (PASS); University of California, San Francisco (UCSF); and University of Toronto (UT) AS studies. Patients: 2576 men aged 40 to 80 years with a GS between 2 and 6 and clinical stage T1 or T2 prostate cancer enrolled between 1995 and 2014. Measurements: PSA levels and biopsy GSs. Results: After variable surveillance intervals and competing treatments were accounted for, estimated risks for biopsy upgrading were similar in the PASS and UT studies but higher in UCSF and lower in JHU studies. All cohorts had a delay of 3 to 5 months in detecting upgrading with biennial biopsies starting after a first confirmatory biopsy versus annual biopsies. Limitation: The model does not account for possible misclassification of biopsy GS. Conclusion: Men in different AS studies have different risks for biopsy upgrading after variable surveillance protocols and competing treatments are accounted for. Despite these differences, the consequences of more versus less frequent biopsies seem to be similar across cohorts. Biennial biopsies seem to be an acceptable alternative to annual biopsies. Primary Funding Source: National Cancer Institute.
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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 | 0.021 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".