Bibliographic record
Abstract
Introduction: Prostate-specific membrane antigen (PSMA) positron emission tomography (PET) is often positive in patients with biochemical failure (BCF) after radical prostatectomy (RP) or radiation therapy (RT), even when conventional imaging (CI) is negative.Methods: The PREP registry is open at five centers across Ontario and enrollment is according to six clinical scenarios.When first initiated (PREP 1), CI was required for all patients.This has been modified (PREP 2) to require CI only when the prostate-specific antigen (PSA) is >10 ng/mL at the time of PSMA PET.Most PSMA PET scans in PREP used 18-fluorine DCFPyL as a radiotracer.The primary endpoint is overall detection rate, with secondary endpoints including detection rate by clinical cohort, patterns of recurrence, and change in planned management based on PSMA PET results.Results: From December 2018 to March 2022, 3967 PSMA PET studies were done; 348 (12%) were repeat scans, mostly to re-evaluate after an initial negative scan or to follow up after a PSMA PET-directed therapy.Median age at enrollment was 71 years.Overall detection rate was 69%, with limited (pelvic only or oligometastatic) disease detected in 57% and extensive metastatic disease in 12% (Table 1).Overall detection rate by PSA level is shown in Table 2. Overall detection rates among men with PSA <10 ng/mL, with or without CI, were similar (64% vs. 70%).The PSMA PET led to a change in planned management in half of cases: 29% changed to local salvage, 19% to systemic therapy, and 5% to observation.Conclusions: The PREP registry is a large, multicenter collection of patients with recurrent prostate cancer who have received PSMA PET imaging prior to salvage treatment.PSMA-avid disease and a change in management were seen in most men.The omission of CI in patients with PSA <10 ng/mL did not seem to dramatically change patterns of disease detection or management change.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.492 | 0.305 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".