The Movember Foundation's GAP3 cohort: a profile of the largest global prostate cancer active surveillance database to date
Bibliographic record
Abstract
OBJECTIVES: The Movember Foundation launched the Global Action Plan Prostate Cancer Active Surveillance (GAP3) initiative to create a global consensus on the selection and monitoring of men with low-risk prostate cancer (PCa) on active surveillance (AS). The aim of this study is to present data on inclusion and follow-up for AS in this unique global AS database. PATIENTS AND METHODS: Between 2014 and 2016, the database was created by combining patient data from 25 established AS cohorts worldwide (USA, Canada, Australasia, UK and Europe). Data on a total of 15 101 patients were included. Descriptive statistics were used to report patients' clinical and demographic characteristics at the time of PCa diagnosis, clinical follow-up, discontinuation of AS and subsequent treatment. Cumulative incidence curves were used to report discontinuation rates over time. RESULTS: At diagnosis, the median (interquartile range [IQR]) patient age was 65 (60-70) years and the median prostate-specific antigen level was 5.4 (4.0-7.3) ng/mL. Most patients had clinical stage T1 disease (71.8%), a biopsy Gleason score of 6 (88.8%) and one tumour-positive biopsy core (60.3%). Patients on AS had a median follow-up time of 2.2 (1.0-5.0) years. After 5, 10 and 15 years of follow-up, respectively, 58%, 39% and 23% of patients were still on AS. The current version of GAP3 has limited data on magnetic resonance imaging (MRI), quality of life and genomic testing. CONCLUSIONS: GAP3 is the largest worldwide collaboration integrating patient data from men with PCa on AS. The results will allow individual patients and clinicians to have greater confidence in the personalized decision to either delay or proceed with active treatment. Longer follow-up and the evaluation of MRI, new genomic markers and patient-related outcomes will result in even more valuable data and eventually in better patient outcomes.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".