A Prospective Provincial Registry of <sup>18</sup>F-PSMA PET/CT for Recurrent Prostate Cancer: Results for 4,135 Men
Bibliographic record
Abstract
The PSMA-PET Registry for Recurrent Prostate Cancer study was initiated in Ontario, Canada, to provide access to and characterize the performance of 18F-prostate-specific membrane antigen (PSMA) PET/CT among men with recurrent prostate cancer. Methods: Between October 2018 and September 2022, 4,135 men were enrolled in PREP. Eligibility included suspected prostate cancer recurrence after prior definitive treatment (radical prostatectomy or radiotherapy). Men were enrolled in 1 of 6 predefined clinical cohorts and imaged with 18F-DCFPyL at 1 of 6 participating sites. Standardized reports delineated sites of recurrence and changes in disease management after PET/CT. Linkage to provincial databases allowed estimation of overall survival (OS) and use of salvage radiotherapy after PET/CT. Results: The median follow-up was 1.8 y. Significant predictors of a positive PET/CT scan on multivariate analysis included a higher prostate-specific antigen level at the time of PET/CT and cohort (highest for cohort 4, whose cancer had progressed during salvage hormone therapy). Significant predictors of management change were type of recurrence (highest for isolated locoregional disease) and higher prostate-specific antigen level. Significant predictors of worse OS included cohort (worst for cohort 4) and extent and type of metastases (worst for mixed bone, lymph, and visceral or extensive metastases). A change in disease management after PET/CT was a significant independent predictor of improved OS rates. Conclusion: PREP facilitated access to 18F-PSMA PET/CT and demonstrated high rates of disease detection. Significant factors associated with survival were clinical scenario, pattern of metastases, and change in disease management after 18F-PSMA PET/CT.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".