PSMA diagnostics and treatments of prostate cancer become mature
Bibliographic record
Abstract
Prostate cancer is the second leading cause of cancer death for men in many parts of the world. Radical prostatectomy (RP) and external beam radiotherapy (EBRT) are effective treatment of localized prostate cancer but many patients recur with raising levels of prostate-specific antigen (PSA). Localized salvage therapies for suspected local failure such as salvage EBRT after RP are most effective during early PSA recurrence. In a Danish national cohort study, salvage EBRT for patients with PSA recurrence after RP undertaken without restaging imaging was effective for approximately half of the patients [1]. Improving on these results requires identification of men with truly localized or oligometastatic recurrence. However, restaging with conventional imaging such as CT and bone scans has limitations. For example, among patients with PSA recurrence after RP, those with a rising PSA < 10 µg/L rarely have positive bone scans. This is problematic because guidelines recommend that patients with PSA recurrence after RP should undergo salvage radiotherapy, while PSA is < 0.5 µg/L [2]. Thus, bone scans cannot guide salvage treatment for most patients with early-phase PSA recurrence after RP. Prostate membrane antigen (PSMA) is highly expressed in poorly differentiated, metastatic, and castration-resistant prostate cancer. In recent years, there has been enormous progress in the use of PSMA for diagnostics and for treatment with the annual number of pubmed hits for the search words “PSMA” and “prostate cancer” increasing nearly five fold over the last 5 years (from 132 to 630). The challenging question is whether PSMA used for diagnostics and treatment of prostate cancer can postpone progression to death and reduce mortality.
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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 | 0.001 |
| 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".